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人工智能AI：Keras PyTorch MXNet TensorFlow PaddlePaddle 深度學習實戰（不定時更新）
集成學習：Bagging、隨機森林、Boosting、GBDT
5.8 《絕地求生》玩家排名預測
1 項目背景
絕地求生(Player unknown's Battlegrounds)，俗稱吃雞，是一款戰術競技型射擊類沙盒遊戲。
這款遊戲是一款大逃殺類型的遊戲，每一局遊戲將有最多100名玩家參與，他們將被投放在絕地島(battlegrounds)上，在遊戲的開始時所有人都一無所有。玩家需要在島上收集各種資源，在不斷縮小的安全區域內對抗其他玩家，讓自己生存到最後。
本作擁有很高的自由度，玩家可以體驗飛機跳傘、開越野車、叢林射擊、搶奪戰利品等玩法，小心四周埋伏的敵人，儘可能成爲最後1個存活的人。
該遊戲中，玩家需要在遊戲地圖上收集各種資源，並在不斷縮小的安全區域內對抗其他玩家，讓自己生存到最後。
2 數據集介紹
本項目中，將爲您提供大量匿名的《絕地求生》遊戲統計數據。
其格式爲每行包含一個玩家的遊戲後統計數據，列爲數據的特徵值。
數據來自所有類型的比賽：單排，雙排，四排；不保證每場比賽有100名人員，每組最多4名成員。
文件說明:
- train_V2.csv - 訓練集
- test_V2.csv - 測試集
數據集局部圖如下圖所示:
數據集中字段解釋：
- Id [用戶id]
- Player’s Id
- groupId [所處小隊id]
- ID to identify a group within a match. If the same group of players plays in different matches, they will have a different groupId each time.
- matchId [該場比賽id]
- ID to identify match. There are no matches that are in both the training and testing set.
- assists [助攻數]
- Number of enemy players this player damaged that were killed by teammates.
- boosts [使用能量,道具數量]
- Number of boost items used.
- damageDealt [總傷害]
- Total damage dealt. Note: Self inflicted damage is subtracted.
- DBNOs [擊倒敵人數量]
- Number of enemy players knocked.
- headshotKills [爆頭數]
- Number of enemy players killed with headshots.
- heals [使用治療藥品數量]
- Number of healing items used.
- killPlace [本廠比賽殺敵排行]
- Ranking in match of number of enemy players killed.
- killPoints [Elo殺敵排名]
- Kills-based external ranking of player. (Think of this as an Elo ranking where only kills matter.) If there is a value other than -1 in rankPoints, then any 0 in killPoints should be treated as a “None”.
- kills [殺敵數]
- Number of enemy players killed.
- killStreaks [連續殺敵數]
- Max number of enemy players killed in a short amount of time.
- longestKill [最遠殺敵距離]
- Longest distance between player and player killed at time of death. This may be misleading, as downing a player and driving away may lead to a large longestKill stat.
- matchDuration [比賽時長]
- Duration of match in seconds.
- matchType [比賽類型(小組人數)]
- String identifying the game mode that the data comes from. The standard modes are “solo”, “duo”, “squad”, “solo-fpp”, “duo-fpp”, and “squad-fpp”; other modes are from events or custom matches.
- maxPlace [本局最差名次]
- Worst placement we have data for in the match. This may not match with numGroups, as sometimes the data skips over placements.
- numGroups [小組數量]
- Number of groups we have data for in the match.
- rankPoints [Elo排名]
- Elo-like ranking of player. This ranking is inconsistent and is being deprecated in the API’s next version, so use with caution. Value of -1 takes place of “None”.
- revives [救活隊員的次數]
- Number of times this player revived teammates.
- rideDistance [駕車距離]
- Total distance traveled in vehicles measured in meters.
- roadKills [駕車殺敵數]
- Number of kills while in a vehicle.
- swimDistance [游泳距離]
- Total distance traveled by swimming measured in meters.
- teamKills [殺死隊友的次數]
- Number of times this player killed a teammate.
- vehicleDestroys [毀壞機動車的數量]
- Number of vehicles destroyed.
- walkDistance [步行距離]
- Total distance traveled on foot measured in meters.
- weaponsAcquired [收集武器的數量]
- Number of weapons picked up.
- winPoints [勝率Elo排名]
- Win-based external ranking of player. (Think of this as an Elo ranking where only winning matters.) If there is a value other than -1 in rankPoints, then any 0 in winPoints should be treated as a “None”.
- winPlacePerc [百分比排名]
- The target of prediction. This is a percentile winning placement, where 1 corresponds to 1st place, and 0 corresponds to last place in the match. It is calculated off of maxPlace, not numGroups, so it is possible to have missing chunks in a match.
3 項目評估方式
你必須創建一個模型，根據他們的最終統計數據預測玩家的排名，從1（第一名）到0（最後一名）。
最後結果通過平均絕對誤差（MAE）進行評估，即通過預測的winPlacePerc和真實的winPlacePerc之間的平均絕對誤差
關於MAE:
- sklearn.metrics.mean_absolute_error
4 代碼實現
項目實現
獲取數據、基本數據信息查看
In [1]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
In [2]:
train = pd.read_csv("./data/train_V2.csv")
In [3]:
train.head()
Out[3]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | revives | rideDistance | roadKills | swimDistance | teamKills | vehicleDestroys | walkDistance | weaponsAcquired | winPoints | winPlacePerc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7f96b2f878858a | 4d4b580de459be | a10357fd1a4a91 | 0 | 0 | 0.00 | 0 | 0 | 0 | 60 | ... | 0 | 0.0000 | 0 | 0.00 | 0 | 0 | 244.80 | 1 | 1466 | 0.4444 |
| 1 | eef90569b9d03c | 684d5656442f9e | aeb375fc57110c | 0 | 0 | 91.47 | 0 | 0 | 0 | 57 | ... | 0 | 0.0045 | 0 | 11.04 | 0 | 0 | 1434.00 | 5 | 0 | 0.6400 |
| 2 | 1eaf90ac73de72 | 6a4a42c3245a74 | 110163d8bb94ae | 1 | 0 | 68.00 | 0 | 0 | 0 | 47 | ... | 0 | 0.0000 | 0 | 0.00 | 0 | 0 | 161.80 | 2 | 0 | 0.7755 |
| 3 | 4616d365dd2853 | a930a9c79cd721 | f1f1f4ef412d7e | 0 | 0 | 32.90 | 0 | 0 | 0 | 75 | ... | 0 | 0.0000 | 0 | 0.00 | 0 | 0 | 202.70 | 3 | 0 | 0.1667 |
| 4 | 315c96c26c9aac | de04010b3458dd | 6dc8ff871e21e6 | 0 | 0 | 100.00 | 0 | 0 | 0 | 45 | ... | 0 | 0.0000 | 0 | 0.00 | 0 | 0 | 49.75 | 2 | 0 | 0.1875 |
5 rows × 29 columns
In [4]:
train.tail()
Out[4]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | revives | rideDistance | roadKills | swimDistance | teamKills | vehicleDestroys | walkDistance | weaponsAcquired | winPoints | winPlacePerc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4446961 | afff7f652dbc10 | d238e426f50de7 | 18492834ce5635 | 0 | 0 | 0.00 | 0 | 0 | 0 | 74 | ... | 0 | 1292.0 | 0 | 0.000 | 0 | 0 | 1019.0 | 3 | 1507 | 0.1786 |
| 4446962 | f4197cf374e6c0 | 408cdb5c46b2ac | ee854b837376d9 | 0 | 1 | 44.15 | 0 | 0 | 0 | 69 | ... | 0 | 0.0 | 0 | 0.000 | 0 | 0 | 81.7 | 6 | 0 | 0.2935 |
| 4446963 | e1948b1295c88a | e26ac84bdf7cef | 6d0cd12784f1ab | 0 | 0 | 59.06 | 0 | 0 | 0 | 66 | ... | 0 | 0.0 | 0 | 2.184 | 0 | 0 | 788.7 | 4 | 0 | 0.4815 |
| 4446964 | cc032cdd73b7ac | c2223f35411394 | c9c701d0ad758a | 0 | 4 | 180.40 | 1 | 1 | 2 | 11 | ... | 2 | 0.0 | 0 | 0.000 | 0 | 0 | 2748.0 | 8 | 0 | 0.8000 |
| 4446965 | 0d8e7ed728b6fd | 8c74f72fedf5ff | 62a16aabcc095c | 0 | 2 | 268.00 | 0 | 0 | 1 | 18 | ... | 0 | 1369.0 | 0 | 0.000 | 0 | 0 | 1244.0 | 5 | 0 | 0.5464 |
5 rows × 29 columns
In [5]:
train.describe()
Out[5]:
| assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | killPoints | kills | killStreaks | ... | revives | rideDistance | roadKills | swimDistance | teamKills | vehicleDestroys | walkDistance | weaponsAcquired | winPoints | winPlacePerc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | ... | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446966e+06 | 4.446965e+06 |
| mean | 2.338149e-01 | 1.106908e+00 | 1.307171e+02 | 6.578755e-01 | 2.268196e-01 | 1.370147e+00 | 4.759935e+01 | 5.050060e+02 | 9.247833e-01 | 5.439551e-01 | ... | 1.646590e-01 | 6.061157e+02 | 3.496091e-03 | 4.509322e+00 | 2.386841e-02 | 7.918208e-03 | 1.154218e+03 | 3.660488e+00 | 6.064601e+02 | 4.728216e-01 |
| std | 5.885731e-01 | 1.715794e+00 | 1.707806e+02 | 1.145743e+00 | 6.021553e-01 | 2.679982e+00 | 2.746294e+01 | 6.275049e+02 | 1.558445e+00 | 7.109721e-01 | ... | 4.721671e-01 | 1.498344e+03 | 7.337297e-02 | 3.050220e+01 | 1.673935e-01 | 9.261157e-02 | 1.183497e+03 | 2.456544e+00 | 7.397004e+02 | 3.074050e-01 |
| min | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| 25% | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 2.400000e+01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.551000e+02 | 2.000000e+00 | 0.000000e+00 | 2.000000e-01 |
| 50% | 0.000000e+00 | 0.000000e+00 | 8.424000e+01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 4.700000e+01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 6.856000e+02 | 3.000000e+00 | 0.000000e+00 | 4.583000e-01 |
| 75% | 0.000000e+00 | 2.000000e+00 | 1.860000e+02 | 1.000000e+00 | 0.000000e+00 | 2.000000e+00 | 7.100000e+01 | 1.172000e+03 | 1.000000e+00 | 1.000000e+00 | ... | 0.000000e+00 | 1.909750e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.976000e+03 | 5.000000e+00 | 1.495000e+03 | 7.407000e-01 |
| max | 2.200000e+01 | 3.300000e+01 | 6.616000e+03 | 5.300000e+01 | 6.400000e+01 | 8.000000e+01 | 1.010000e+02 | 2.170000e+03 | 7.200000e+01 | 2.000000e+01 | ... | 3.900000e+01 | 4.071000e+04 | 1.800000e+01 | 3.823000e+03 | 1.200000e+01 | 5.000000e+00 | 2.578000e+04 | 2.360000e+02 | 2.013000e+03 | 1.000000e+00 |
8 rows × 25 columns
In [6]:
train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4446966 entries, 0 to 4446965
Data columns (total 29 columns):
Id object
groupId object
matchId object
assists int64
boosts int64
damageDealt float64
DBNOs int64
headshotKills int64
heals int64
killPlace int64
killPoints int64
kills int64
killStreaks int64
longestKill float64
matchDuration int64
matchType object
maxPlace int64
numGroups int64
rankPoints int64
revives int64
rideDistance float64
roadKills int64
swimDistance float64
teamKills int64
vehicleDestroys int64
walkDistance float64
weaponsAcquired int64
winPoints int64
winPlacePerc float64
dtypes: float64(6), int64(19), object(4)
memory usage: 983.9+ MB
In [7]:
# 查看一共要多少條數據
train.shape
Out[7]:
(4446966, 29)
In [8]:
# 有多少場比賽
np.unique(train["matchId"]).shape
Out[8]:
(47965,)
In [9]:
# 有多少支隊伍
np.unique(train["groupId"]).shape
Out[9]:
(2026745,)
數據基本處理
數據缺失值處理
In [10]:
# 判斷哪列有缺失值,發現只有winPlacePerc有
np.any(train.isnull())
Out[10]:
Id False
groupId False
matchId False
assists False
boosts False
damageDealt False
DBNOs False
headshotKills False
heals False
killPlace False
killPoints False
kills False
killStreaks False
longestKill False
matchDuration False
matchType False
maxPlace False
numGroups False
rankPoints False
revives False
rideDistance False
roadKills False
swimDistance False
teamKills False
vehicleDestroys False
walkDistance False
weaponsAcquired False
winPoints False
winPlacePerc True
dtype: bool
In [11]:
# 查找缺失值
train[train["winPlacePerc"].isnull()]
Out[11]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | revives | rideDistance | roadKills | swimDistance | teamKills | vehicleDestroys | walkDistance | weaponsAcquired | winPoints | winPlacePerc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2744604 | f70c74418bb064 | 12dfbede33f92b | 224a123c53e008 | 0 | 0 | 0.0 | 0 | 0 | 0 | 1 | ... | 0 | 0.0 | 0 | 0.0 | 0 | 0 | 0.0 | 0 | 0 | NaN |
1 rows × 29 columns
In [12]:
# 刪除
train = train.drop(2744604)
In [13]:
train.shape
Out[13]:
(4446965, 29)
特徵數據規範化處理
查看每場比賽參加的人數
In [14]:
count = train.groupby("matchId")["matchId"].transform("count")
In [15]:
train["playersJoined"] = count
In [16]:
train.head()
Out[16]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | rideDistance | roadKills | swimDistance | teamKills | vehicleDestroys | walkDistance | weaponsAcquired | winPoints | winPlacePerc | playersJoined | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7f96b2f878858a | 4d4b580de459be | a10357fd1a4a91 | 0 | 0 | 0.00 | 0 | 0 | 0 | 60 | ... | 0.0000 | 0 | 0.00 | 0 | 0 | 244.80 | 1 | 1466 | 0.4444 | 96 |
| 1 | eef90569b9d03c | 684d5656442f9e | aeb375fc57110c | 0 | 0 | 91.47 | 0 | 0 | 0 | 57 | ... | 0.0045 | 0 | 11.04 | 0 | 0 | 1434.00 | 5 | 0 | 0.6400 | 91 |
| 2 | 1eaf90ac73de72 | 6a4a42c3245a74 | 110163d8bb94ae | 1 | 0 | 68.00 | 0 | 0 | 0 | 47 | ... | 0.0000 | 0 | 0.00 | 0 | 0 | 161.80 | 2 | 0 | 0.7755 | 98 |
| 3 | 4616d365dd2853 | a930a9c79cd721 | f1f1f4ef412d7e | 0 | 0 | 32.90 | 0 | 0 | 0 | 75 | ... | 0.0000 | 0 | 0.00 | 0 | 0 | 202.70 | 3 | 0 | 0.1667 | 91 |
| 4 | 315c96c26c9aac | de04010b3458dd | 6dc8ff871e21e6 | 0 | 0 | 100.00 | 0 | 0 | 0 | 45 | ... | 0.0000 | 0 | 0.00 | 0 | 0 | 49.75 | 2 | 0 | 0.1875 | 97 |
5 rows × 30 columns
In [17]:
train["playersJoined"].sort_values().head()
Out[17]:
1206365 2
2109739 2
3956552 5
3620228 5
696000 5
Name: playersJoined, dtype: int64
In [18]:
plt.figure(figsize=(20, 8))
sns.countplot(train["playersJoined"])
plt.grid()
plt.show()
In [19]:
# train[train["playersJoined"]>=75]["playersJoined"]
In [20]:
plt.figure(figsize=(20, 8))
sns.countplot(train[train["playersJoined"]>=75]["playersJoined"])
plt.grid()
plt.show()
規範化輸出部分數據
In [21]:
train["killsNorm"] = train["kills"] * ((100-train["playersJoined"])/100+1)
In [22]:
train["damageDealtNorm"] = train["damageDealt"] * ((100-train["playersJoined"])/100+1)
train["maxPlaceNorm"] = train["maxPlace"] * ((100-train["playersJoined"])/100+1)
train["matchDurationNorm"] = train["matchDuration"] * ((100-train["playersJoined"])/100+1)
In [23]:
train.head()
Out[23]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | vehicleDestroys | walkDistance | weaponsAcquired | winPoints | winPlacePerc | playersJoined | killsNorm | damageDealtNorm | maxPlaceNorm | matchDurationNorm | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7f96b2f878858a | 4d4b580de459be | a10357fd1a4a91 | 0 | 0 | 0.00 | 0 | 0 | 0 | 60 | ... | 0 | 244.80 | 1 | 1466 | 0.4444 | 96 | 0.00 | 0.0000 | 29.12 | 1358.24 |
| 1 | eef90569b9d03c | 684d5656442f9e | aeb375fc57110c | 0 | 0 | 91.47 | 0 | 0 | 0 | 57 | ... | 0 | 1434.00 | 5 | 0 | 0.6400 | 91 | 0.00 | 99.7023 | 28.34 | 1936.93 |
| 2 | 1eaf90ac73de72 | 6a4a42c3245a74 | 110163d8bb94ae | 1 | 0 | 68.00 | 0 | 0 | 0 | 47 | ... | 0 | 161.80 | 2 | 0 | 0.7755 | 98 | 0.00 | 69.3600 | 51.00 | 1344.36 |
| 3 | 4616d365dd2853 | a930a9c79cd721 | f1f1f4ef412d7e | 0 | 0 | 32.90 | 0 | 0 | 0 | 75 | ... | 0 | 202.70 | 3 | 0 | 0.1667 | 91 | 0.00 | 35.8610 | 33.79 | 1565.24 |
| 4 | 315c96c26c9aac | de04010b3458dd | 6dc8ff871e21e6 | 0 | 0 | 100.00 | 0 | 0 | 0 | 45 | ... | 0 | 49.75 | 2 | 0 | 0.1875 | 97 | 1.03 | 103.0000 | 99.91 | 1466.72 |
5 rows × 34 columns
In [24]:
# 比較經過規範化的特徵值和原始特徵值的值
to_show = ['Id', 'kills','killsNorm','damageDealt', 'damageDealtNorm', 'maxPlace', 'maxPlaceNorm', 'matchDuration', 'matchDurationNorm']
train[to_show][0:11]
Out[24]:
| Id | kills | killsNorm | damageDealt | damageDealtNorm | maxPlace | maxPlaceNorm | matchDuration | matchDurationNorm | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 7f96b2f878858a | 0 | 0.00 | 0.000 | 0.00000 | 28 | 29.12 | 1306 | 1358.24 |
| 1 | eef90569b9d03c | 0 | 0.00 | 91.470 | 99.70230 | 26 | 28.34 | 1777 | 1936.93 |
| 2 | 1eaf90ac73de72 | 0 | 0.00 | 68.000 | 69.36000 | 50 | 51.00 | 1318 | 1344.36 |
| 3 | 4616d365dd2853 | 0 | 0.00 | 32.900 | 35.86100 | 31 | 33.79 | 1436 | 1565.24 |
| 4 | 315c96c26c9aac | 1 | 1.03 | 100.000 | 103.00000 | 97 | 99.91 | 1424 | 1466.72 |
| 5 | ff79c12f326506 | 1 | 1.05 | 100.000 | 105.00000 | 28 | 29.40 | 1395 | 1464.75 |
| 6 | 95959be0e21ca3 | 0 | 0.00 | 0.000 | 0.00000 | 28 | 28.84 | 1316 | 1355.48 |
| 7 | 311b84c6ff4390 | 0 | 0.00 | 8.538 | 8.87952 | 96 | 99.84 | 1967 | 2045.68 |
| 8 | 1a68204ccf9891 | 0 | 0.00 | 51.600 | 53.14800 | 28 | 28.84 | 1375 | 1416.25 |
| 9 | e5bb5a43587253 | 0 | 0.00 | 37.270 | 38.38810 | 29 | 29.87 | 1930 | 1987.90 |
| 10 | 2b574d43972813 | 0 | 0.00 | 28.380 | 28.66380 | 29 | 29.29 | 1811 | 1829.11 |
部分變量合成
In [25]:
train["healsandboosts"] = train["heals"] + train["boosts"]
In [57]:
train[["heals", "boosts", "healsandboosts"]].tail(10)
Out[57]:
| heals | boosts | healsandboosts | |
|---|---|---|---|
| 4446956 | 1 | 0 | 1 |
| 4446957 | 0 | 1 | 1 |
| 4446958 | 0 | 0 | 0 |
| 4446959 | 0 | 0 | 0 |
| 4446960 | 0 | 0 | 0 |
| 4446961 | 0 | 0 | 0 |
| 4446962 | 0 | 1 | 1 |
| 4446963 | 0 | 0 | 0 |
| 4446964 | 2 | 4 | 6 |
| 4446965 | 1 | 2 | 3 |
異常值處理
異常值處理：刪除有擊殺，但是完全沒有移動的玩家
In [58]:
train["totalDistance"] = train["rideDistance"] + train["walkDistance"] + train["swimDistance"]
In [59]:
train.head()
Out[59]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | winPoints | winPlacePerc | playersJoined | killsNorm | damageDealtNorm | maxPlaceNorm | matchDurationNorm | healsandboosts | totalDistance | killwithoutMoving | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7f96b2f878858a | 4d4b580de459be | a10357fd1a4a91 | 0 | 0 | 0.00 | 0 | 0 | 0 | 60 | ... | 1466 | 0.4444 | 96 | 0.00 | 0.0000 | 29.12 | 1358.24 | 0 | 244.8000 | False |
| 1 | eef90569b9d03c | 684d5656442f9e | aeb375fc57110c | 0 | 0 | 91.47 | 0 | 0 | 0 | 57 | ... | 0 | 0.6400 | 91 | 0.00 | 99.7023 | 28.34 | 1936.93 | 0 | 1445.0445 | False |
| 2 | 1eaf90ac73de72 | 6a4a42c3245a74 | 110163d8bb94ae | 1 | 0 | 68.00 | 0 | 0 | 0 | 47 | ... | 0 | 0.7755 | 98 | 0.00 | 69.3600 | 51.00 | 1344.36 | 0 | 161.8000 | False |
| 3 | 4616d365dd2853 | a930a9c79cd721 | f1f1f4ef412d7e | 0 | 0 | 32.90 | 0 | 0 | 0 | 75 | ... | 0 | 0.1667 | 91 | 0.00 | 35.8610 | 33.79 | 1565.24 | 0 | 202.7000 | False |
| 4 | 315c96c26c9aac | de04010b3458dd | 6dc8ff871e21e6 | 0 | 0 | 100.00 | 0 | 0 | 0 | 45 | ... | 0 | 0.1875 | 97 | 1.03 | 103.0000 | 99.91 | 1466.72 | 0 | 49.7500 | False |
5 rows × 37 columns
In [60]:
# (train["kills"] > 0) & (train["totalDistance"] == 0)
In [61]:
train["killwithoutMoving"] = (train["kills"] > 0) & (train["totalDistance"] == 0)
In [64]:
train[train["killwithoutMoving"] == True].head()
Out[64]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | winPoints | winPlacePerc | playersJoined | killsNorm | damageDealtNorm | maxPlaceNorm | matchDurationNorm | healsandboosts | totalDistance | killwithoutMoving | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1824 | b538d514ef2476 | 0eb2ce2f43f9d6 | 35e7d750e442e2 | 0 | 0 | 593.0 | 0 | 0 | 3 | 18 | ... | 0 | 0.8571 | 58 | 8.52 | 842.060 | 21.30 | 842.06 | 3 | 0.0 | True |
| 6673 | 6d3a61da07b7cb | 2d8119b1544f87 | 904cecf36217df | 2 | 0 | 346.6 | 0 | 0 | 6 | 33 | ... | 0 | 0.6000 | 42 | 4.74 | 547.628 | 17.38 | 2834.52 | 6 | 0.0 | True |
| 11892 | 550398a8f33db7 | c3fd0e2abab0af | db6f6d1f0d4904 | 2 | 0 | 1750.0 | 0 | 4 | 5 | 3 | ... | 0 | 0.8947 | 21 | 35.80 | 3132.500 | 35.80 | 1607.42 | 5 | 0.0 | True |
| 14631 | 58d690ee461e9d | ea5b6630b33d67 | dbf34301df5e53 | 0 | 0 | 157.8 | 0 | 0 | 0 | 69 | ... | 1500 | 0.0000 | 73 | 1.27 | 200.406 | 24.13 | 1014.73 | 0 | 0.0 | True |
| 15591 | 49b61fc963d632 | 0f5c5f19d9cc21 | 904cecf36217df | 0 | 0 | 100.0 | 0 | 1 | 0 | 37 | ... | 0 | 0.3000 | 42 | 1.58 | 158.000 | 17.38 | 2834.52 | 0 | 0.0 | True |
5 rows × 37 columns
In [65]:
train[train["killwithoutMoving"] == True].shape
Out[65]:
(1535, 37)
In [66]:
train[train["killwithoutMoving"] == True].index
Out[66]:
Int64Index([ 1824, 6673, 11892, 14631, 15591, 20881, 23298,
24640, 25659, 30079,
...
4426500, 4429697, 4432954, 4436511, 4437516, 4440232, 4440898,
4440927, 4441511, 4446682],
dtype='int64', length=1535)
In [67]:
train.drop(train[train["killwithoutMoving"] == True].index, inplace=True)
In [68]:
train.shape
Out[68]:
(4445430, 37)
異常值處理：刪除駕車殺敵數異常的數據
In [70]:
# train["roadKills"] > 10
In [71]:
train.drop(train[train["roadKills"] > 10].index, inplace=True)
In [72]:
train.shape
Out[72]:
(4445426, 37)
異常值處理：刪除玩家在一局中殺敵數超過30人的數據
In [75]:
train[train["kills"] > 30].head()
Out[75]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | winPoints | winPlacePerc | playersJoined | killsNorm | damageDealtNorm | maxPlaceNorm | matchDurationNorm | healsandboosts | totalDistance | killwithoutMoving | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57978 | 9d8253e21ccbbd | ef7135ed856cd8 | 37f05e2a01015f | 9 | 0 | 3725.0 | 0 | 7 | 0 | 2 | ... | 1500 | 0.8571 | 16 | 64.40 | 6854.00 | 14.72 | 3308.32 | 0 | 48.82 | False |
| 87793 | 45f76442384931 | b3627758941d34 | 37f05e2a01015f | 8 | 0 | 3087.0 | 0 | 8 | 27 | 3 | ... | 1500 | 1.0000 | 16 | 57.04 | 5680.08 | 14.72 | 3308.32 | 27 | 780.70 | False |
| 156599 | 746aa7eabf7c86 | 5723e7d8250da3 | f900de1ec39fa5 | 21 | 0 | 5479.0 | 0 | 12 | 7 | 4 | ... | 0 | 0.7000 | 11 | 90.72 | 10355.31 | 20.79 | 3398.22 | 7 | 23.71 | False |
| 160254 | 15622257cb44e2 | 1a513eeecfe724 | db413c7c48292c | 1 | 0 | 4033.0 | 0 | 40 | 0 | 1 | ... | 1500 | 1.0000 | 62 | 57.96 | 5565.54 | 11.04 | 1164.72 | 0 | 718.30 | False |
| 180189 | 1355613d43e2d0 | f863cd38c61dbf | 39c442628f5df5 | 5 | 0 | 3171.0 | 0 | 6 | 15 | 1 | ... | 0 | 1.0000 | 11 | 66.15 | 5993.19 | 17.01 | 3394.44 | 15 | 71.51 | False |
5 rows × 37 columns
In [76]:
train.drop(train[train["kills"] > 30].index, inplace=True)
train.shape
Out[76]:
(4445331, 37)
異常值處理：刪除爆頭率異常數據
In [79]:
train["headshot_rate"] = train["headshotKills"]/train["kills"]
train["headshot_rate"].head()
Out[79]:
0 NaN
1 NaN
2 NaN
3 NaN
4 0.0
Name: headshot_rate, dtype: float64
In [81]:
train["headshot_rate"] = train["headshot_rate"].fillna(0)
In [82]:
train.head()
Out[82]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | winPlacePerc | playersJoined | killsNorm | damageDealtNorm | maxPlaceNorm | matchDurationNorm | healsandboosts | totalDistance | killwithoutMoving | headshot_rate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7f96b2f878858a | 4d4b580de459be | a10357fd1a4a91 | 0 | 0 | 0.00 | 0 | 0 | 0 | 60 | ... | 0.4444 | 96 | 0.00 | 0.0000 | 29.12 | 1358.24 | 0 | 244.8000 | False | 0.0 |
| 1 | eef90569b9d03c | 684d5656442f9e | aeb375fc57110c | 0 | 0 | 91.47 | 0 | 0 | 0 | 57 | ... | 0.6400 | 91 | 0.00 | 99.7023 | 28.34 | 1936.93 | 0 | 1445.0445 | False | 0.0 |
| 2 | 1eaf90ac73de72 | 6a4a42c3245a74 | 110163d8bb94ae | 1 | 0 | 68.00 | 0 | 0 | 0 | 47 | ... | 0.7755 | 98 | 0.00 | 69.3600 | 51.00 | 1344.36 | 0 | 161.8000 | False | 0.0 |
| 3 | 4616d365dd2853 | a930a9c79cd721 | f1f1f4ef412d7e | 0 | 0 | 32.90 | 0 | 0 | 0 | 75 | ... | 0.1667 | 91 | 0.00 | 35.8610 | 33.79 | 1565.24 | 0 | 202.7000 | False | 0.0 |
| 4 | 315c96c26c9aac | de04010b3458dd | 6dc8ff871e21e6 | 0 | 0 | 100.00 | 0 | 0 | 0 | 45 | ... | 0.1875 | 97 | 1.03 | 103.0000 | 99.91 | 1466.72 | 0 | 49.7500 | False | 0.0 |
5 rows × 38 columns
In [85]:
train[(train["headshot_rate"] == 1) & (train["kills"] > 9)].head()
Out[85]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | winPlacePerc | playersJoined | killsNorm | damageDealtNorm | maxPlaceNorm | matchDurationNorm | healsandboosts | totalDistance | killwithoutMoving | headshot_rate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 281570 | ab9d7168570927 | add05ebde0214c | e016a873339c7b | 2 | 3 | 1212.0 | 8 | 10 | 0 | 1 | ... | 0.8462 | 93 | 10.70 | 1296.84 | 28.89 | 1522.61 | 3 | 2939.0 | False | 1.0 |
| 346124 | 044d18fc42fc75 | fc1dbc2df6a887 | 628107d4c41084 | 3 | 5 | 1620.0 | 13 | 11 | 3 | 1 | ... | 1.0000 | 96 | 11.44 | 1684.80 | 28.08 | 1796.08 | 8 | 8142.0 | False | 1.0 |
| 871244 | e668a25f5488e3 | 5ba8feabfb2a23 | f6e6581e03ba4f | 0 | 4 | 1365.0 | 9 | 13 | 0 | 1 | ... | 1.0000 | 98 | 13.26 | 1392.30 | 27.54 | 1280.10 | 4 | 2105.0 | False | 1.0 |
| 908815 | 566d8218b705aa | a9b056478d71b2 | 3a41552d553583 | 2 | 5 | 1535.0 | 10 | 10 | 3 | 1 | ... | 0.9630 | 95 | 10.50 | 1611.75 | 29.40 | 1929.90 | 8 | 7948.0 | False | 1.0 |
| 963463 | 1bd6fd288df4f0 | 90584ffa22fe15 | ba2de992ec7bb8 | 2 | 6 | 1355.0 | 12 | 10 | 2 | 1 | ... | 1.0000 | 96 | 10.40 | 1409.20 | 28.08 | 1473.68 | 8 | 3476.0 | False | 1.0 |
5 rows × 38 columns
In [86]:
train[(train["headshot_rate"] == 1) & (train["kills"] > 9)].index
Out[86]:
Int64Index([ 281570, 346124, 871244, 908815, 963463, 1079403, 1167959,
1348164, 1380385, 1483199, 1581850, 1622232, 1753322, 2256755,
2375749, 2647056, 2825200, 3288424, 3594399, 3926325, 4036281,
4351048, 4387092, 4428741],
dtype='int64')
In [87]:
train.drop(train[(train["headshot_rate"] == 1) & (train["kills"] > 9)].index, inplace=True)
train.shape
Out[87]:
(4445307, 38)
異常值處理：刪除最遠殺敵距離異常數據
In [89]:
train[train["longestKill"] >=1000]
Out[89]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | winPlacePerc | playersJoined | killsNorm | damageDealtNorm | maxPlaceNorm | matchDurationNorm | healsandboosts | totalDistance | killwithoutMoving | headshot_rate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202281 | 88e2af7d78af5a | 34ddeede52c042 | 4346bc63bc67fa | 0 | 3 | 783.9 | 5 | 1 | 1 | 5 | ... | 0.9231 | 88 | 4.48 | 877.968 | 30.24 | 2087.68 | 4 | 3775.20 | False | 0.250000 |
| 240005 | 41c2f5c0699807 | 9faecf87ab4275 | 634edab75860b3 | 5 | 0 | 1284.0 | 8 | 5 | 7 | 18 | ... | 0.5385 | 29 | 18.81 | 2195.640 | 23.94 | 2236.68 | 7 | 48.87 | False | 0.454545 |
| 324313 | ef390c152bcc3d | 30fd444be3bbc1 | 4f7f8d6cf558b4 | 2 | 0 | 1028.0 | 0 | 0 | 0 | 9 | ... | 1.0000 | 51 | 14.90 | 1531.720 | 19.37 | 1040.02 | 0 | 2981.00 | False | 0.000000 |
| 656553 | 9948b058562163 | c8cb8491112bf6 | 0104eeb664494d | 6 | 0 | 1410.0 | 17 | 5 | 0 | 3 | ... | 0.6000 | 41 | 25.44 | 2241.900 | 9.54 | 1734.69 | 0 | 29.21 | False | 0.312500 |
| 803632 | 4e7e6c74e3c57d | 94698690918933 | da91b0c3d875f8 | 0 | 0 | 196.8 | 0 | 0 | 0 | 51 | ... | 0.0000 | 61 | 1.39 | 273.552 | 11.12 | 654.69 | 0 | 3159.00 | False | 0.000000 |
| 895411 | 1f5ba6e0cfb968 | 512ea24b831be3 | 5fb0d8b1fc16cf | 4 | 0 | 1012.0 | 11 | 5 | 0 | 5 | ... | 0.9091 | 86 | 11.40 | 1153.680 | 13.68 | 1163.94 | 0 | 569.50 | False | 0.500000 |
| 1172437 | 303a93cfa1f46c | 8795d39fd0df86 | 9c8962b58bb3e3 | 2 | 1 | 329.3 | 0 | 0 | 2 | 45 | ... | 0.2857 | 58 | 4.26 | 467.606 | 11.36 | 825.02 | 3 | 832.50 | False | 0.000000 |
| 1209416 | 528659ff1c1aec | 7d1ba83423551d | ea9386587d5888 | 0 | 6 | 1640.0 | 0 | 7 | 0 | 1 | ... | 0.9412 | 52 | 22.20 | 2427.200 | 76.96 | 1827.80 | 6 | 2848.00 | False | 0.466667 |
| 1642712 | 91966848e08e2f | 0ee4fbd27657c9 | 17dea22cefe62a | 3 | 2 | 2103.0 | 0 | 4 | 11 | 11 | ... | 0.5000 | 28 | 39.56 | 3617.160 | 25.80 | 3092.56 | 13 | 235.30 | False | 0.173913 |
| 2015559 | 5ff0c1a9fab2ba | 2d8119b1544f87 | 904cecf36217df | 3 | 3 | 1302.0 | 0 | 6 | 5 | 15 | ... | 0.6000 | 42 | 17.38 | 2057.160 | 17.38 | 2834.52 | 8 | 133.20 | False | 0.545455 |
| 2122128 | 42df3102cb540b | 7d9b2be15b355b | 610d78f3affd2e | 5 | 0 | 2500.0 | 0 | 7 | 1 | 2 | ... | 0.0000 | 10 | 41.80 | 4750.000 | 3.80 | 3416.20 | 1 | 464.50 | False | 0.318182 |
| 2152425 | 4b9f61bac5eb0a | bc717b964f3bbe | 838cb9a3c94598 | 3 | 0 | 945.4 | 0 | 0 | 0 | 11 | ... | 0.5714 | 60 | 18.20 | 1323.560 | 11.20 | 1673.00 | 0 | 844.70 | False | 0.000000 |
| 2592718 | 24e0fec84c18e9 | 8404855ca02e48 | e886a8ebb702cf | 7 | 0 | 1684.0 | 0 | 4 | 7 | 11 | ... | 0.5714 | 26 | 22.62 | 2930.160 | 38.28 | 3118.08 | 7 | 4851.00 | False | 0.307692 |
| 2981715 | 7f77051c7cef52 | d6579a630399b5 | 4784f7d9a06b51 | 3 | 5 | 1025.0 | 5 | 2 | 5 | 2 | ... | 1.0000 | 93 | 6.42 | 1096.750 | 50.29 | 1453.06 | 10 | 4085.96 | False | 0.333333 |
| 3081503 | f19a76e8d7ac52 | 624d65c529f87c | de19b70121c40f | 3 | 0 | 1038.0 | 0 | 0 | 0 | 32 | ... | 0.8571 | 57 | 8.58 | 1484.340 | 11.44 | 945.23 | 0 | 270.00 | False | 0.000000 |
| 3255171 | 5524c154448425 | 674195558ad41b | db6f6d1f0d4904 | 1 | 0 | 1355.0 | 0 | 2 | 0 | 9 | ... | 0.5789 | 21 | 25.06 | 2425.450 | 35.80 | 1607.42 | 0 | 1039.00 | False | 0.142857 |
| 3304284 | d0c286ce498e17 | 17fdd45e612bab | 3eaaa2f7a360fe | 7 | 0 | 2330.0 | 0 | 2 | 0 | 2 | ... | 1.0000 | 53 | 29.40 | 3425.100 | 26.46 | 1321.53 | 0 | 68.02 | False | 0.100000 |
| 3320960 | 0040e53dfe7b5d | 650661c2351eb7 | 2daabf3a7852e6 | 0 | 0 | 399.0 | 2 | 0 | 6 | 14 | ... | 0.0000 | 15 | 7.40 | 738.150 | 14.80 | 2763.90 | 6 | 5481.00 | False | 0.000000 |
| 3552532 | db638834c62f6f | 0614b611d6a935 | ff80300f8262f5 | 2 | 0 | 517.0 | 0 | 0 | 0 | 10 | ... | 0.0000 | 30 | 8.50 | 878.900 | 6.80 | 612.00 | 0 | 1344.88 | False | 0.000000 |
| 4332473 | d8857d3d7e31b6 | 085de7a36897e6 | 42f997c16d8a0e | 5 | 0 | 1685.0 | 11 | 3 | 18 | 8 | ... | 0.9091 | 26 | 27.84 | 2931.900 | 20.88 | 3119.82 | 18 | 523.30 | False | 0.187500 |
20 rows × 38 columns
In [90]:
train[train["longestKill"] >=1000].index
Out[90]:
Int64Index([ 202281, 240005, 324313, 656553, 803632, 895411, 1172437,
1209416, 1642712, 2015559, 2122128, 2152425, 2592718, 2981715,
3081503, 3255171, 3304284, 3320960, 3552532, 4332473],
dtype='int64')
In [91]:
train.drop(train[train["longestKill"] >=1000].index, inplace=True)
train.shape
Out[91]:
(4445287, 38)
異常值處理：刪除關於運動距離的異常值
In [93]:
# 行走
train[train["walkDistance"] >=10000].index
Out[93]:
Int64Index([ 23026, 34344, 49312, 68590, 94400, 125103, 136421,
136476, 154080, 154128,
...
4181311, 4230073, 4259976, 4284974, 4288445, 4306598, 4370543,
4380785, 4405009, 4415088],
dtype='int64', length=219)
In [94]:
train.drop(train[train["walkDistance"] >=10000].index, inplace=True)
train.shape
Out[94]:
(4445068, 38)
In [95]:
# 載具
train[train["rideDistance"] >=20000].index
Out[95]:
Int64Index([ 28588, 63015, 70507, 72763, 95276, 140097, 297186,
371098, 403647, 426708,
...
4154459, 4191491, 4239725, 4248221, 4256764, 4270943, 4301013,
4386384, 4404738, 4440261],
dtype='int64', length=150)
In [96]:
train.drop(train[train["rideDistance"] >=20000].index, inplace=True)
train.shape
Out[96]:
(4444918, 38)
In [97]:
# 游泳
train[train["swimDistance"] >=2000].index
Out[97]:
Int64Index([ 177973, 274258, 1005337, 1195818, 1227362, 1889163, 2065940,
2327586, 2784855, 3359439, 3513522, 4132225],
dtype='int64')
In [98]:
train.drop(train[train["swimDistance"] >=20000].index, inplace=True)
train.shape
Out[98]:
(4444918, 38)
異常值處理：武器收集異常值處理
In [99]:
train[train["weaponsAcquired"] >=80].index
Out[99]:
Int64Index([ 233643, 588387, 1437471, 1449293, 1592744, 1834515, 2373240,
2442962, 2743408, 2749693, 2797867, 2973445, 2977084, 2982525,
3230315, 3405716, 3951710, 4022031, 4288517],
dtype='int64')
In [100]:
train.drop(train[train["weaponsAcquired"] >=80].index, inplace=True)
train.shape
Out[100]:
(4444899, 38)
異常值處理：刪除使用治療藥品數量異常值
In [101]:
train[train["heals"] >=80].index
Out[101]:
Int64Index([4262662], dtype='int64')
In [102]:
train.drop(train[train["heals"] >=80].index, inplace=True)
train.shape
Out[102]:
(4444898, 38)
類別型數據處理
比賽類型one-hot處理
In [104]:
train["matchType"].unique()
Out[104]:
array(['squad-fpp', 'duo', 'solo-fpp', 'squad', 'duo-fpp', 'solo',
'normal-squad-fpp', 'crashfpp', 'flaretpp', 'normal-solo-fpp',
'flarefpp', 'normal-duo-fpp', 'normal-duo', 'normal-squad',
'crashtpp', 'normal-solo'], dtype=object)
In [106]:
train = pd.get_dummies(train, columns=["matchType"])
In [107]:
train.head()
Out[107]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | matchType_normal-duo | matchType_normal-duo-fpp | matchType_normal-solo | matchType_normal-solo-fpp | matchType_normal-squad | matchType_normal-squad-fpp | matchType_solo | matchType_solo-fpp | matchType_squad | matchType_squad-fpp | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7f96b2f878858a | 4d4b580de459be | a10357fd1a4a91 | 0 | 0 | 0.00 | 0 | 0 | 0 | 60 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1 | eef90569b9d03c | 684d5656442f9e | aeb375fc57110c | 0 | 0 | 91.47 | 0 | 0 | 0 | 57 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | 1eaf90ac73de72 | 6a4a42c3245a74 | 110163d8bb94ae | 1 | 0 | 68.00 | 0 | 0 | 0 | 47 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 4616d365dd2853 | a930a9c79cd721 | f1f1f4ef412d7e | 0 | 0 | 32.90 | 0 | 0 | 0 | 75 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 4 | 315c96c26c9aac | de04010b3458dd | 6dc8ff871e21e6 | 0 | 0 | 100.00 | 0 | 0 | 0 | 45 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
5 rows × 53 columns
In [108]:
matchType_encoding = train.filter(regex="matchType")
In [109]:
matchType_encoding.head()
Out[109]:
| matchType_crashfpp | matchType_crashtpp | matchType_duo | matchType_duo-fpp | matchType_flarefpp | matchType_flaretpp | matchType_normal-duo | matchType_normal-duo-fpp | matchType_normal-solo | matchType_normal-solo-fpp | matchType_normal-squad | matchType_normal-squad-fpp | matchType_solo | matchType_solo-fpp | matchType_squad | matchType_squad-fpp | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
對groupId,matchId等數據進行處理
In [110]:
train["groupId"].head()
Out[110]:
0 4d4b580de459be
1 684d5656442f9e
2 6a4a42c3245a74
3 a930a9c79cd721
4 de04010b3458dd
Name: groupId, dtype: object
In [112]:
# train["groupId"].astype("category")
In [113]:
train["groupId"] = train["groupId"].astype("category")
In [114]:
train["groupId_cat"] = train["groupId"].cat.codes
In [116]:
train["groupId_cat"].head()
Out[116]:
0 613619
1 827616
2 843307
3 1340122
4 1757411
Name: groupId_cat, dtype: int32
In [117]:
train["matchId"] = train["matchId"].astype("category")
train["matchId_cat"] = train["matchId"].cat.codes
train["matchId_cat"].head()
Out[117]:
0 30085
1 32751
2 3143
3 45260
4 20531
Name: matchId_cat, dtype: int32
In [118]:
train.head()
Out[118]:
| Id | groupId | matchId | assists | boosts | damageDealt | DBNOs | headshotKills | heals | killPlace | ... | matchType_normal-solo | matchType_normal-solo-fpp | matchType_normal-squad | matchType_normal-squad-fpp | matchType_solo | matchType_solo-fpp | matchType_squad | matchType_squad-fpp | groupId_cat | matchId_cat | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7f96b2f878858a | 4d4b580de459be | a10357fd1a4a91 | 0 | 0 | 0.00 | 0 | 0 | 0 | 60 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 613619 | 30085 |
| 1 | eef90569b9d03c | 684d5656442f9e | aeb375fc57110c | 0 | 0 | 91.47 | 0 | 0 | 0 | 57 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 827616 | 32751 |
| 2 | 1eaf90ac73de72 | 6a4a42c3245a74 | 110163d8bb94ae | 1 | 0 | 68.00 | 0 | 0 | 0 | 47 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 843307 | 3143 |
| 3 | 4616d365dd2853 | a930a9c79cd721 | f1f1f4ef412d7e | 0 | 0 | 32.90 | 0 | 0 | 0 | 75 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1340122 | 45260 |
| 4 | 315c96c26c9aac | de04010b3458dd | 6dc8ff871e21e6 | 0 | 0 | 100.00 | 0 | 0 | 0 | 45 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1757411 | 20531 |
5 rows × 55 columns
In [120]:
train.drop(["groupId", "matchId"], axis=1, inplace=True)
數據截取
取部分數據進行使用（100000）
In [122]:
df_sample = train.sample(100000)
In [123]:
df_sample.shape
Out[123]:
(100000, 53)
確定特徵值和目標值
In [124]:
df = df_sample.drop(["winPlacePerc", "Id"], axis=1)
y = df_sample["winPlacePerc"]
In [125]:
df.shape
Out[125]:
(100000, 51)
In [126]:
y.shape
Out[126]:
(100000,)
分割訓練集和測試集
In [127]:
from sklearn.model_selection import train_test_split
In [128]:
X_train, X_valid, y_train, y_valid = train_test_split(df, y, test_size=0.2)
In [129]:
X_train.shape
Out[129]:
(80000, 51)
In [131]:
y_train.shape
Out[131]:
(80000,)
機器學習（模型訓練）和評估
In [132]:
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
使用隨機森林對模型進行訓練
初步使用隨機森林進行模型訓練
In [134]:
m1 = RandomForestRegressor(n_estimators=40,
min_samples_leaf=3,
max_features='sqrt',
n_jobs=-1)
m1.fit(X_train, y_train)
Out[134]:
RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
max_depth=None, max_features='sqrt', max_leaf_nodes=None,
max_samples=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=3,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=40, n_jobs=-1, oob_score=False,
random_state=None, verbose=0, warm_start=False)
In [137]:
y_pre = m1.predict(X_valid)
m1.score(X_valid, y_valid)
Out[137]:
0.907159951456783
In [138]:
mean_absolute_error(y_valid, y_pre)
Out[138]:
0.06647584387091089
再次使用隨機森林，進行模型訓練
In [139]:
m1.feature_importances_
Out[139]:
array([1.87658119e-03, 8.63746064e-02, 2.57685962e-02, 2.27532062e-03,
8.80290888e-04, 2.81013428e-02, 2.35573150e-01, 2.07083462e-03,
1.22714874e-02, 1.09702183e-02, 2.53353780e-02, 1.03767737e-02,
6.77020063e-03, 7.45172312e-03, 4.28638708e-03, 3.28563034e-03,
2.12925123e-02, 1.99967979e-05, 3.98400033e-03, 1.36372746e-04,
1.32592980e-04, 1.71940999e-01, 4.20790087e-02, 2.52040557e-03,
6.33213818e-03, 7.30402941e-03, 1.11477263e-02, 7.52171932e-03,
1.19465432e-02, 5.30382552e-02, 1.81348675e-01, 0.00000000e+00,
2.22747761e-03, 3.44612223e-05, 0.00000000e+00, 2.02507233e-04,
5.97533128e-04, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
3.54286394e-05, 0.00000000e+00, 4.94182811e-07, 0.00000000e+00,
3.11300574e-04, 1.93840925e-04, 9.02659739e-04, 1.04955041e-03,
9.87217660e-04, 4.53628891e-03, 4.50774311e-03])
In [140]:
imp_df = pd.DataFrame({"cols":df.columns, "imp":m1.feature_importances_})
In [141]:
imp_df.head()
Out[141]:
| cols | imp | |
|---|---|---|
| 0 | assists | 0.001877 |
| 1 | boosts | 0.086375 |
| 2 | damageDealt | 0.025769 |
| 3 | DBNOs | 0.002275 |
| 4 | headshotKills | 0.000880 |
In [142]:
imp_df = imp_df.sort_values("imp", ascending=False)
In [143]:
imp_df.head()
Out[143]:
| cols | imp | |
|---|---|---|
| 6 | killPlace | 0.235573 |
| 30 | totalDistance | 0.181349 |
| 21 | walkDistance | 0.171941 |
| 1 | boosts | 0.086375 |
| 29 | healsandboosts | 0.053038 |
In [145]:
imp_df[:20].plot("cols", "imp", figsize=(20, 8), kind="barh")
Out[145]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1500a080>
In [146]:
to_keep = imp_df[imp_df.imp > 0.005].cols
In [148]:
to_keep.shape
Out[148]:
(20,)
In [150]:
df_keep = df[to_keep]
In [151]:
X_train, X_valid, y_train, y_valid = train_test_split(df_keep, y, test_size=0.2)
In [153]:
X_train.shape
Out[153]:
(80000, 20)
In [154]:
m2 = RandomForestRegressor(n_estimators=40,
min_samples_leaf=3,
max_features='sqrt',
n_jobs=-1)
m2.fit(X_train, y_train)
Out[154]:
RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
max_depth=None, max_features='sqrt', max_leaf_nodes=None,
max_samples=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=3,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=40, n_jobs=-1, oob_score=False,
random_state=None, verbose=0, warm_start=False)
In [158]:
y_pre = m2.predict(X_valid)
m2.score(X_valid, y_valid)
Out[158]:
0.9125654968172906
In [159]:
mean_absolute_error(y_valid, y_pre)
Out[159]:
0.06408683094647326
使用lightGBM對模型進行訓練
In [160]:
X_train, X_valid, y_train, y_valid = train_test_split(df, y, test_size=0.2)
In [161]:
X_train.shape
Out[161]:
(80000, 51)
模型初次嘗試
In [162]:
import lightgbm as lgb
In [163]:
gbm = lgb.LGBMRegressor(objective="regression", num_leaves=31, learning_rate=0.05, n_estimators=20)
gbm.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], eval_metric="l1", early_stopping_rounds=5)
[1] valid_0's l1: 0.255801 valid_0's l2: 0.0863836
Training until validation scores don't improve for 5 rounds
[2] valid_0's l1: 0.244604 valid_0's l2: 0.0792314
[3] valid_0's l1: 0.234038 valid_0's l2: 0.072761
[4] valid_0's l1: 0.224123 valid_0's l2: 0.0669453
[5] valid_0's l1: 0.214716 valid_0's l2: 0.0616647
[6] valid_0's l1: 0.205802 valid_0's l2: 0.0568409
[7] valid_0's l1: 0.197424 valid_0's l2: 0.0525102
[8] valid_0's l1: 0.189497 valid_0's l2: 0.0485595
[9] valid_0's l1: 0.18208 valid_0's l2: 0.0450087
[10] valid_0's l1: 0.175038 valid_0's l2: 0.0417809
[11] valid_0's l1: 0.168411 valid_0's l2: 0.0388494
[12] valid_0's l1: 0.162014 valid_0's l2: 0.0361473
[13] valid_0's l1: 0.156139 valid_0's l2: 0.0337388
[14] valid_0's l1: 0.150548 valid_0's l2: 0.031546
[15] valid_0's l1: 0.145259 valid_0's l2: 0.0295381
[16] valid_0's l1: 0.140261 valid_0's l2: 0.0277049
[17] valid_0's l1: 0.135596 valid_0's l2: 0.0260668
[18] valid_0's l1: 0.131269 valid_0's l2: 0.0245903
[19] valid_0's l1: 0.127159 valid_0's l2: 0.0232428
[20] valid_0's l1: 0.123315 valid_0's l2: 0.0220185
Did not meet early stopping. Best iteration is:
[20] valid_0's l1: 0.123315 valid_0's l2: 0.0220185
Out[163]:
LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
importance_type='split', learning_rate=0.05, max_depth=-1,
min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
n_estimators=20, n_jobs=-1, num_leaves=31, objective='regression',
random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,
subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
In [164]:
y_pre = gbm.predict(X_valid, num_iteration=gbm.best_iteration_)
In [165]:
mean_absolute_error(y_valid, y_pre)
Out[165]:
0.12331524150224461
模型二次調優
In [166]:
from sklearn.model_selection import GridSearchCV
In [167]:
estimator = lgb.LGBMRegressor(num_leaves=31)
param_grid = {
"learning_rate":[0.01, 0.1, 1],
"n_estimators":[40, 60, 80, 100, 200, 300]
}
gbm = GridSearchCV(estimator, param_grid, cv=5, n_jobs=-1)
gbm.fit(X_train, y_train)
Out[167]:
GridSearchCV(cv=5, error_score=nan,
estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None,
colsample_bytree=1.0,
importance_type='split', learning_rate=0.1,
max_depth=-1, min_child_samples=20,
min_child_weight=0.001, min_split_gain=0.0,
n_estimators=100, n_jobs=-1, num_leaves=31,
objective=None, random_state=None,
reg_alpha=0.0, reg_lambda=0.0, silent=True,
subsample=1.0, subsample_for_bin=200000,
subsample_freq=0),
iid='deprecated', n_jobs=-1,
param_grid={'learning_rate': [0.01, 0.1, 1],
'n_estimators': [40, 60, 80, 100, 200, 300]},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)
In [168]:
y_pre = gbm.predict(X_valid)
mean_absolute_error(y_valid, y_pre)
Out[168]:
0.05685004010605751
In [169]:
gbm.best_params_
Out[169]:
{'learning_rate': 0.1, 'n_estimators': 300}
模型三次調優
In [173]:
# n_estimators
scores = []
n_estimators = [100, 300, 500, 800]
for nes in n_estimators:
lgbm = lgb.LGBMRegressor(boosting_type='gbdt',
num_leaves=31,
max_depth=5,
learning_rate=0.1,
n_estimators=nes,
min_child_samples=20,
n_jobs=-1)
lgbm.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], eval_metric="l1", early_stopping_rounds=5)
y_pre = lgbm.predict(X_valid)
mae = mean_absolute_error(y_valid, y_pre)
scores.append(mae)
print("本次結果輸出的mae值是:\n", mae)
[1] valid_0's l1: 0.244506 valid_0's l2: 0.0790859
Training until validation scores don't improve for 5 rounds
[2] valid_0's l1: 0.223857 valid_0's l2: 0.0667341
[3] valid_0's l1: 0.205626 valid_0's l2: 0.0566941
[4] valid_0's l1: 0.189298 valid_0's l2: 0.0484539
[5] valid_0's l1: 0.175104 valid_0's l2: 0.0417921
[6] valid_0's l1: 0.16227 valid_0's l2: 0.036285
[7] valid_0's l1: 0.150898 valid_0's l2: 0.0317221
[8] valid_0's l1: 0.140883 valid_0's l2: 0.0280196
[9] valid_0's l1: 0.131939 valid_0's l2: 0.0249613
[10] valid_0's l1: 0.124375 valid_0's l2: 0.0224944
[11] valid_0's l1: 0.117467 valid_0's l2: 0.0204263
[12] valid_0's l1: 0.110957 valid_0's l2: 0.0185453
[13] valid_0's l1: 0.105077 valid_0's l2: 0.016929
[14] valid_0's l1: 0.100272 valid_0's l2: 0.0157152
[15] valid_0's l1: 0.0960833 valid_0's l2: 0.0147431
[16] valid_0's l1: 0.0920567 valid_0's l2: 0.0137979
[17] valid_0's l1: 0.088268 valid_0's l2: 0.0129121
[18] valid_0's l1: 0.0854499 valid_0's l2: 0.0123437
[19] valid_0's l1: 0.0827065 valid_0's l2: 0.0117755
[20] valid_0's l1: 0.0800967 valid_0's l2: 0.0112162
[21] valid_0's l1: 0.0781237 valid_0's l2: 0.0108399
[22] valid_0's l1: 0.0763776 valid_0's l2: 0.0105204
[23] valid_0's l1: 0.074906 valid_0's l2: 0.010265
[24] valid_0's l1: 0.0732985 valid_0's l2: 0.00994854
[25] valid_0's l1: 0.0720481 valid_0's l2: 0.00970346
[26] valid_0's l1: 0.0710837 valid_0's l2: 0.00953733
[27] valid_0's l1: 0.0703066 valid_0's l2: 0.00939487
[28] valid_0's l1: 0.0694075 valid_0's l2: 0.009231
[29] valid_0's l1: 0.0685866 valid_0's l2: 0.00907982
[30] valid_0's l1: 0.06779 valid_0's l2: 0.0089391
[31] valid_0's l1: 0.067074 valid_0's l2: 0.00880224
[32] valid_0's l1: 0.0665149 valid_0's l2: 0.00871073
[33] valid_0's l1: 0.0659205 valid_0's l2: 0.00858992
[34] valid_0's l1: 0.0652426 valid_0's l2: 0.00843778
[35] valid_0's l1: 0.0648768 valid_0's l2: 0.00836372
[36] valid_0's l1: 0.0645306 valid_0's l2: 0.00830614
[37] valid_0's l1: 0.0640175 valid_0's l2: 0.00818645
[38] valid_0's l1: 0.0637947 valid_0's l2: 0.00814636
[39] valid_0's l1: 0.0635605 valid_0's l2: 0.00810447
[40] valid_0's l1: 0.0633449 valid_0's l2: 0.00806796
[41] valid_0's l1: 0.0631446 valid_0's l2: 0.00802436
[42] valid_0's l1: 0.0627575 valid_0's l2: 0.00792715
[43] valid_0's l1: 0.062611 valid_0's l2: 0.00789809
[44] valid_0's l1: 0.0623997 valid_0's l2: 0.00785422
[45] valid_0's l1: 0.0622728 valid_0's l2: 0.00783369
[46] valid_0's l1: 0.0621336 valid_0's l2: 0.00781042
[47] valid_0's l1: 0.0619906 valid_0's l2: 0.00777463
[48] valid_0's l1: 0.0618556 valid_0's l2: 0.00774471
[49] valid_0's l1: 0.0617449 valid_0's l2: 0.00772713
[50] valid_0's l1: 0.0616454 valid_0's l2: 0.00770345
[51] valid_0's l1: 0.0615492 valid_0's l2: 0.00768433
[52] valid_0's l1: 0.0614452 valid_0's l2: 0.00766572
[53] valid_0's l1: 0.0613725 valid_0's l2: 0.00764408
[54] valid_0's l1: 0.0612796 valid_0's l2: 0.00762834
[55] valid_0's l1: 0.0611706 valid_0's l2: 0.00760066
[56] valid_0's l1: 0.0611121 valid_0's l2: 0.00759012
[57] valid_0's l1: 0.0610472 valid_0's l2: 0.00757864
[58] valid_0's l1: 0.0609801 valid_0's l2: 0.00756664
[59] valid_0's l1: 0.0608993 valid_0's l2: 0.00755207
[60] valid_0's l1: 0.0608206 valid_0's l2: 0.00753343
[61] valid_0's l1: 0.0607751 valid_0's l2: 0.00752435
[62] valid_0's l1: 0.0607163 valid_0's l2: 0.00751128
[63] valid_0's l1: 0.0605746 valid_0's l2: 0.00747752
[64] valid_0's l1: 0.0604651 valid_0's l2: 0.0074507
[65] valid_0's l1: 0.0604263 valid_0's l2: 0.00744162
[66] valid_0's l1: 0.0603599 valid_0's l2: 0.00742889
[67] valid_0's l1: 0.0602701 valid_0's l2: 0.00741169
[68] valid_0's l1: 0.0602334 valid_0's l2: 0.00739977
[69] valid_0's l1: 0.060112 valid_0's l2: 0.007374
[70] valid_0's l1: 0.0600257 valid_0's l2: 0.00735234
[71] valid_0's l1: 0.059928 valid_0's l2: 0.00732775
[72] valid_0's l1: 0.0598568 valid_0's l2: 0.00731406
[73] valid_0's l1: 0.0598019 valid_0's l2: 0.007307
[74] valid_0's l1: 0.0597175 valid_0's l2: 0.00728248
[75] valid_0's l1: 0.0596246 valid_0's l2: 0.00726385
[76] valid_0's l1: 0.0596029 valid_0's l2: 0.00725546
[77] valid_0's l1: 0.0595231 valid_0's l2: 0.00723235
[78] valid_0's l1: 0.0595014 valid_0's l2: 0.00722831
[79] valid_0's l1: 0.0594712 valid_0's l2: 0.00722155
[80] valid_0's l1: 0.0594512 valid_0's l2: 0.00721585
[81] valid_0's l1: 0.0594197 valid_0's l2: 0.00720856
[82] valid_0's l1: 0.0593932 valid_0's l2: 0.00720475
[83] valid_0's l1: 0.0593157 valid_0's l2: 0.00718559
[84] valid_0's l1: 0.0592926 valid_0's l2: 0.00718174
[85] valid_0's l1: 0.0592627 valid_0's l2: 0.00717558
[86] valid_0's l1: 0.0592466 valid_0's l2: 0.00716984
[87] valid_0's l1: 0.0592043 valid_0's l2: 0.00716193
[88] valid_0's l1: 0.0591298 valid_0's l2: 0.00714342
[89] valid_0's l1: 0.059097 valid_0's l2: 0.00713607
[90] valid_0's l1: 0.0589982 valid_0's l2: 0.00711745
[91] valid_0's l1: 0.0589505 valid_0's l2: 0.00710413
[92] valid_0's l1: 0.0588884 valid_0's l2: 0.00708708
[93] valid_0's l1: 0.0588084 valid_0's l2: 0.00707062
[94] valid_0's l1: 0.0587967 valid_0's l2: 0.00707269
[95] valid_0's l1: 0.0587689 valid_0's l2: 0.00706628
[96] valid_0's l1: 0.058749 valid_0's l2: 0.00705746
[97] valid_0's l1: 0.0587121 valid_0's l2: 0.00704977
[98] valid_0's l1: 0.0586712 valid_0's l2: 0.00703874
[99] valid_0's l1: 0.05861 valid_0's l2: 0.0070237
[100] valid_0's l1: 0.0585889 valid_0's l2: 0.00701449
Did not meet early stopping. Best iteration is:
[100] valid_0's l1: 0.0585889 valid_0's l2: 0.00701449
本次結果輸出的mae值是:
0.05858885784299778
[1] valid_0's l1: 0.244506 valid_0's l2: 0.0790859
Training until validation scores don't improve for 5 rounds
[2] valid_0's l1: 0.223857 valid_0's l2: 0.0667341
[3] valid_0's l1: 0.205626 valid_0's l2: 0.0566941
[4] valid_0's l1: 0.189298 valid_0's l2: 0.0484539
[5] valid_0's l1: 0.175104 valid_0's l2: 0.0417921
[6] valid_0's l1: 0.16227 valid_0's l2: 0.036285
[7] valid_0's l1: 0.150898 valid_0's l2: 0.0317221
[8] valid_0's l1: 0.140883 valid_0's l2: 0.0280196
[9] valid_0's l1: 0.131939 valid_0's l2: 0.0249613
[10] valid_0's l1: 0.124375 valid_0's l2: 0.0224944
[11] valid_0's l1: 0.117467 valid_0's l2: 0.0204263
[12] valid_0's l1: 0.110957 valid_0's l2: 0.0185453
[13] valid_0's l1: 0.105077 valid_0's l2: 0.016929
[14] valid_0's l1: 0.100272 valid_0's l2: 0.0157152
[15] valid_0's l1: 0.0960833 valid_0's l2: 0.0147431
[16] valid_0's l1: 0.0920567 valid_0's l2: 0.0137979
[17] valid_0's l1: 0.088268 valid_0's l2: 0.0129121
[18] valid_0's l1: 0.0854499 valid_0's l2: 0.0123437
[19] valid_0's l1: 0.0827065 valid_0's l2: 0.0117755
[20] valid_0's l1: 0.0800967 valid_0's l2: 0.0112162
[21] valid_0's l1: 0.0781237 valid_0's l2: 0.0108399
[22] valid_0's l1: 0.0763776 valid_0's l2: 0.0105204
[23] valid_0's l1: 0.074906 valid_0's l2: 0.010265
[24] valid_0's l1: 0.0732985 valid_0's l2: 0.00994854
[25] valid_0's l1: 0.0720481 valid_0's l2: 0.00970346
[26] valid_0's l1: 0.0710837 valid_0's l2: 0.00953733
[27] valid_0's l1: 0.0703066 valid_0's l2: 0.00939487
[28] valid_0's l1: 0.0694075 valid_0's l2: 0.009231
[29] valid_0's l1: 0.0685866 valid_0's l2: 0.00907982
[30] valid_0's l1: 0.06779 valid_0's l2: 0.0089391
[31] valid_0's l1: 0.067074 valid_0's l2: 0.00880224
[32] valid_0's l1: 0.0665149 valid_0's l2: 0.00871073
[33] valid_0's l1: 0.0659205 valid_0's l2: 0.00858992
[34] valid_0's l1: 0.0652426 valid_0's l2: 0.00843778
[35] valid_0's l1: 0.0648768 valid_0's l2: 0.00836372
[36] valid_0's l1: 0.0645306 valid_0's l2: 0.00830614
[37] valid_0's l1: 0.0640175 valid_0's l2: 0.00818645
[38] valid_0's l1: 0.0637947 valid_0's l2: 0.00814636
[39] valid_0's l1: 0.0635605 valid_0's l2: 0.00810447
[40] valid_0's l1: 0.0633449 valid_0's l2: 0.00806796
[41] valid_0's l1: 0.0631446 valid_0's l2: 0.00802436
[42] valid_0's l1: 0.0627575 valid_0's l2: 0.00792715
[43] valid_0's l1: 0.062611 valid_0's l2: 0.00789809
[44] valid_0's l1: 0.0623997 valid_0's l2: 0.00785422
[45] valid_0's l1: 0.0622728 valid_0's l2: 0.00783369
[46] valid_0's l1: 0.0621336 valid_0's l2: 0.00781042
[47] valid_0's l1: 0.0619906 valid_0's l2: 0.00777463
[48] valid_0's l1: 0.0618556 valid_0's l2: 0.00774471
[49] valid_0's l1: 0.0617449 valid_0's l2: 0.00772713
[50] valid_0's l1: 0.0616454 valid_0's l2: 0.00770345
[51] valid_0's l1: 0.0615492 valid_0's l2: 0.00768433
[52] valid_0's l1: 0.0614452 valid_0's l2: 0.00766572
[53] valid_0's l1: 0.0613725 valid_0's l2: 0.00764408
[54] valid_0's l1: 0.0612796 valid_0's l2: 0.00762834
[55] valid_0's l1: 0.0611706 valid_0's l2: 0.00760066
[56] valid_0's l1: 0.0611121 valid_0's l2: 0.00759012
[57] valid_0's l1: 0.0610472 valid_0's l2: 0.00757864
[58] valid_0's l1: 0.0609801 valid_0's l2: 0.00756664
[59] valid_0's l1: 0.0608993 valid_0's l2: 0.00755207
[60] valid_0's l1: 0.0608206 valid_0's l2: 0.00753343
[61] valid_0's l1: 0.0607751 valid_0's l2: 0.00752435
[62] valid_0's l1: 0.0607163 valid_0's l2: 0.00751128
[63] valid_0's l1: 0.0605746 valid_0's l2: 0.00747752
[64] valid_0's l1: 0.0604651 valid_0's l2: 0.0074507
[65] valid_0's l1: 0.0604263 valid_0's l2: 0.00744162
[66] valid_0's l1: 0.0603599 valid_0's l2: 0.00742889
[67] valid_0's l1: 0.0602701 valid_0's l2: 0.00741169
[68] valid_0's l1: 0.0602334 valid_0's l2: 0.00739977
[69] valid_0's l1: 0.060112 valid_0's l2: 0.007374
[70] valid_0's l1: 0.0600257 valid_0's l2: 0.00735234
[71] valid_0's l1: 0.059928 valid_0's l2: 0.00732775
[72] valid_0's l1: 0.0598568 valid_0's l2: 0.00731406
[73] valid_0's l1: 0.0598019 valid_0's l2: 0.007307
[74] valid_0's l1: 0.0597175 valid_0's l2: 0.00728248
[75] valid_0's l1: 0.0596246 valid_0's l2: 0.00726385
[76] valid_0's l1: 0.0596029 valid_0's l2: 0.00725546
[77] valid_0's l1: 0.0595231 valid_0's l2: 0.00723235
[78] valid_0's l1: 0.0595014 valid_0's l2: 0.00722831
[79] valid_0's l1: 0.0594712 valid_0's l2: 0.00722155
[80] valid_0's l1: 0.0594512 valid_0's l2: 0.00721585
[81] valid_0's l1: 0.0594197 valid_0's l2: 0.00720856
[82] valid_0's l1: 0.0593932 valid_0's l2: 0.00720475
[83] valid_0's l1: 0.0593157 valid_0's l2: 0.00718559
[84] valid_0's l1: 0.0592926 valid_0's l2: 0.00718174
[85] valid_0's l1: 0.0592627 valid_0's l2: 0.00717558
[86] valid_0's l1: 0.0592466 valid_0's l2: 0.00716984
[87] valid_0's l1: 0.0592043 valid_0's l2: 0.00716193
[88] valid_0's l1: 0.0591298 valid_0's l2: 0.00714342
[89] valid_0's l1: 0.059097 valid_0's l2: 0.00713607
[90] valid_0's l1: 0.0589982 valid_0's l2: 0.00711745
[91] valid_0's l1: 0.0589505 valid_0's l2: 0.00710413
[92] valid_0's l1: 0.0588884 valid_0's l2: 0.00708708
[93] valid_0's l1: 0.0588084 valid_0's l2: 0.00707062
[94] valid_0's l1: 0.0587967 valid_0's l2: 0.00707269
[95] valid_0's l1: 0.0587689 valid_0's l2: 0.00706628
[96] valid_0's l1: 0.058749 valid_0's l2: 0.00705746
[97] valid_0's l1: 0.0587121 valid_0's l2: 0.00704977
[98] valid_0's l1: 0.0586712 valid_0's l2: 0.00703874
[99] valid_0's l1: 0.05861 valid_0's l2: 0.0070237
[100] valid_0's l1: 0.0585889 valid_0's l2: 0.00701449
[101] valid_0's l1: 0.0585771 valid_0's l2: 0.00701003
[102] valid_0's l1: 0.0585635 valid_0's l2: 0.0070058
[103] valid_0's l1: 0.0585479 valid_0's l2: 0.00700123
[104] valid_0's l1: 0.0585384 valid_0's l2: 0.00699934
[105] valid_0's l1: 0.0585209 valid_0's l2: 0.00699689
[106] valid_0's l1: 0.0585063 valid_0's l2: 0.00699365
[107] valid_0's l1: 0.0584905 valid_0's l2: 0.00698833
[108] valid_0's l1: 0.0584738 valid_0's l2: 0.00698361
[109] valid_0's l1: 0.0584563 valid_0's l2: 0.00697852
[110] valid_0's l1: 0.0584379 valid_0's l2: 0.00697721
[111] valid_0's l1: 0.0584333 valid_0's l2: 0.00697385
[112] valid_0's l1: 0.0584316 valid_0's l2: 0.00697371
[113] valid_0's l1: 0.0584076 valid_0's l2: 0.00696515
[114] valid_0's l1: 0.0583903 valid_0's l2: 0.00696145
[115] valid_0's l1: 0.0583817 valid_0's l2: 0.00695705
[116] valid_0's l1: 0.0583585 valid_0's l2: 0.00695171
[117] valid_0's l1: 0.0583329 valid_0's l2: 0.00694439
[118] valid_0's l1: 0.0582953 valid_0's l2: 0.00693451
[119] valid_0's l1: 0.0582302 valid_0's l2: 0.00691471
[120] valid_0's l1: 0.0582053 valid_0's l2: 0.00690717
[121] valid_0's l1: 0.058165 valid_0's l2: 0.00690191
[122] valid_0's l1: 0.0581507 valid_0's l2: 0.00689919
[123] valid_0's l1: 0.0581202 valid_0's l2: 0.00689344
[124] valid_0's l1: 0.0580842 valid_0's l2: 0.00688742
[125] valid_0's l1: 0.0580695 valid_0's l2: 0.00688381
[126] valid_0's l1: 0.0580556 valid_0's l2: 0.0068812
[127] valid_0's l1: 0.0580404 valid_0's l2: 0.00687439
[128] valid_0's l1: 0.0580276 valid_0's l2: 0.00687128
[129] valid_0's l1: 0.0579982 valid_0's l2: 0.00686654
[130] valid_0's l1: 0.0579886 valid_0's l2: 0.00686163
[131] valid_0's l1: 0.0579795 valid_0's l2: 0.00686265
[132] valid_0's l1: 0.0579773 valid_0's l2: 0.00686113
[133] valid_0's l1: 0.0579718 valid_0's l2: 0.00686023
[134] valid_0's l1: 0.0579558 valid_0's l2: 0.00685794
[135] valid_0's l1: 0.0579452 valid_0's l2: 0.00685478
[136] valid_0's l1: 0.0579375 valid_0's l2: 0.00685219
[137] valid_0's l1: 0.0579311 valid_0's l2: 0.00685004
[138] valid_0's l1: 0.0578929 valid_0's l2: 0.0068395
[139] valid_0's l1: 0.0578863 valid_0's l2: 0.00683737
[140] valid_0's l1: 0.0578618 valid_0's l2: 0.00682973
[141] valid_0's l1: 0.057814 valid_0's l2: 0.00682006
[142] valid_0's l1: 0.0577865 valid_0's l2: 0.00681178
[143] valid_0's l1: 0.0577836 valid_0's l2: 0.00681092
[144] valid_0's l1: 0.0577732 valid_0's l2: 0.00680807
[145] valid_0's l1: 0.05775 valid_0's l2: 0.00680476
[146] valid_0's l1: 0.0577295 valid_0's l2: 0.00679983
[147] valid_0's l1: 0.0577274 valid_0's l2: 0.00679906
[148] valid_0's l1: 0.0577282 valid_0's l2: 0.00680071
[149] valid_0's l1: 0.0577027 valid_0's l2: 0.00679154
[150] valid_0's l1: 0.0577023 valid_0's l2: 0.00679031
[151] valid_0's l1: 0.0576895 valid_0's l2: 0.0067869
[152] valid_0's l1: 0.0576863 valid_0's l2: 0.00678669
[153] valid_0's l1: 0.057681 valid_0's l2: 0.00678298
[154] valid_0's l1: 0.0576461 valid_0's l2: 0.00677587
[155] valid_0's l1: 0.0576281 valid_0's l2: 0.00676922
[156] valid_0's l1: 0.0576071 valid_0's l2: 0.00676582
[157] valid_0's l1: 0.0575831 valid_0's l2: 0.00676433
[158] valid_0's l1: 0.0575486 valid_0's l2: 0.00675594
[159] valid_0's l1: 0.0575371 valid_0's l2: 0.00675428
[160] valid_0's l1: 0.0575191 valid_0's l2: 0.00675119
[161] valid_0's l1: 0.0575124 valid_0's l2: 0.00675132
[162] valid_0's l1: 0.0575015 valid_0's l2: 0.00674851
[163] valid_0's l1: 0.0574785 valid_0's l2: 0.00674612
[164] valid_0's l1: 0.0574678 valid_0's l2: 0.00674357
[165] valid_0's l1: 0.0574633 valid_0's l2: 0.00674366
[166] valid_0's l1: 0.0574458 valid_0's l2: 0.00673905
[167] valid_0's l1: 0.0574112 valid_0's l2: 0.00672946
[168] valid_0's l1: 0.0573985 valid_0's l2: 0.00672684
[169] valid_0's l1: 0.0573967 valid_0's l2: 0.00672339
[170] valid_0's l1: 0.0573916 valid_0's l2: 0.00672143
[171] valid_0's l1: 0.0573821 valid_0's l2: 0.00671896
[172] valid_0's l1: 0.0573703 valid_0's l2: 0.00671675
[173] valid_0's l1: 0.0573778 valid_0's l2: 0.00671835
[174] valid_0's l1: 0.0573707 valid_0's l2: 0.00671666
[175] valid_0's l1: 0.0573572 valid_0's l2: 0.00671479
[176] valid_0's l1: 0.0573574 valid_0's l2: 0.0067135
[177] valid_0's l1: 0.0573542 valid_0's l2: 0.00671297
[178] valid_0's l1: 0.057355 valid_0's l2: 0.00671458
[179] valid_0's l1: 0.0573403 valid_0's l2: 0.00671148
[180] valid_0's l1: 0.0573278 valid_0's l2: 0.00670699
[181] valid_0's l1: 0.0573277 valid_0's l2: 0.00670656
[182] valid_0's l1: 0.0573192 valid_0's l2: 0.00670432
[183] valid_0's l1: 0.0573012 valid_0's l2: 0.00670044
[184] valid_0's l1: 0.0573018 valid_0's l2: 0.00669917
[185] valid_0's l1: 0.0572933 valid_0's l2: 0.00669791
[186] valid_0's l1: 0.0572937 valid_0's l2: 0.00669628
[187] valid_0's l1: 0.0572959 valid_0's l2: 0.006696
[188] valid_0's l1: 0.0572946 valid_0's l2: 0.00669529
[189] valid_0's l1: 0.057284 valid_0's l2: 0.00669419
[190] valid_0's l1: 0.0572756 valid_0's l2: 0.00669183
[191] valid_0's l1: 0.0572536 valid_0's l2: 0.00668716
[192] valid_0's l1: 0.0572549 valid_0's l2: 0.0066889
[193] valid_0's l1: 0.0572407 valid_0's l2: 0.00668565
[194] valid_0's l1: 0.0572137 valid_0's l2: 0.00668092
[195] valid_0's l1: 0.0572176 valid_0's l2: 0.00668088
[196] valid_0's l1: 0.0571987 valid_0's l2: 0.00667881
[197] valid_0's l1: 0.0571904 valid_0's l2: 0.00667394
[198] valid_0's l1: 0.0571825 valid_0's l2: 0.00667086
[199] valid_0's l1: 0.0571814 valid_0's l2: 0.0066715
[200] valid_0's l1: 0.0571754 valid_0's l2: 0.0066712
[201] valid_0's l1: 0.0571679 valid_0's l2: 0.00666813
[202] valid_0's l1: 0.0571538 valid_0's l2: 0.00666858
[203] valid_0's l1: 0.0571505 valid_0's l2: 0.00666737
[204] valid_0's l1: 0.0571496 valid_0's l2: 0.00666642
[205] valid_0's l1: 0.0571438 valid_0's l2: 0.00666369
[206] valid_0's l1: 0.0571522 valid_0's l2: 0.0066647
[207] valid_0's l1: 0.0571443 valid_0's l2: 0.00666353
[208] valid_0's l1: 0.057147 valid_0's l2: 0.00666341
[209] valid_0's l1: 0.0571413 valid_0's l2: 0.00666194
[210] valid_0's l1: 0.0571264 valid_0's l2: 0.00666014
[211] valid_0's l1: 0.0571122 valid_0's l2: 0.00665585
[212] valid_0's l1: 0.0571118 valid_0's l2: 0.00665518
[213] valid_0's l1: 0.0571002 valid_0's l2: 0.00665338
[214] valid_0's l1: 0.0570964 valid_0's l2: 0.00665241
[215] valid_0's l1: 0.05709 valid_0's l2: 0.00665055
[216] valid_0's l1: 0.0570856 valid_0's l2: 0.00665057
[217] valid_0's l1: 0.0570826 valid_0's l2: 0.00665004
[218] valid_0's l1: 0.0570747 valid_0's l2: 0.00664898
[219] valid_0's l1: 0.057068 valid_0's l2: 0.00664791
[220] valid_0's l1: 0.05707 valid_0's l2: 0.00664691
[221] valid_0's l1: 0.0570675 valid_0's l2: 0.00664523
[222] valid_0's l1: 0.0570426 valid_0's l2: 0.00664251
[223] valid_0's l1: 0.0570167 valid_0's l2: 0.00663508
[224] valid_0's l1: 0.0570101 valid_0's l2: 0.00663261
[225] valid_0's l1: 0.0570103 valid_0's l2: 0.00663457
[226] valid_0's l1: 0.0569972 valid_0's l2: 0.00663136
[227] valid_0's l1: 0.0569882 valid_0's l2: 0.00663009
[228] valid_0's l1: 0.0569898 valid_0's l2: 0.00662954
[229] valid_0's l1: 0.0569865 valid_0's l2: 0.00662705
[230] valid_0's l1: 0.0569757 valid_0's l2: 0.00662507
[231] valid_0's l1: 0.0569731 valid_0's l2: 0.00662383
[232] valid_0's l1: 0.0569676 valid_0's l2: 0.00662384
[233] valid_0's l1: 0.0569664 valid_0's l2: 0.00662459
[234] valid_0's l1: 0.0569611 valid_0's l2: 0.00662457
[235] valid_0's l1: 0.0569633 valid_0's l2: 0.00662413
[236] valid_0's l1: 0.0569615 valid_0's l2: 0.00662337
[237] valid_0's l1: 0.0569616 valid_0's l2: 0.00662352
[238] valid_0's l1: 0.0569541 valid_0's l2: 0.00662248
[239] valid_0's l1: 0.0569478 valid_0's l2: 0.0066219
[240] valid_0's l1: 0.0569487 valid_0's l2: 0.00662202
[241] valid_0's l1: 0.0569517 valid_0's l2: 0.00662222
[242] valid_0's l1: 0.0569443 valid_0's l2: 0.00662104
[243] valid_0's l1: 0.0569249 valid_0's l2: 0.00661558
[244] valid_0's l1: 0.0569245 valid_0's l2: 0.00661384
[245] valid_0's l1: 0.0569247 valid_0's l2: 0.00661414
[246] valid_0's l1: 0.0569229 valid_0's l2: 0.00661358
[247] valid_0's l1: 0.0569195 valid_0's l2: 0.00661233
[248] valid_0's l1: 0.0569077 valid_0's l2: 0.00660972
[249] valid_0's l1: 0.0569058 valid_0's l2: 0.00660907
[250] valid_0's l1: 0.0569022 valid_0's l2: 0.00660765
[251] valid_0's l1: 0.056903 valid_0's l2: 0.00660776
[252] valid_0's l1: 0.0569011 valid_0's l2: 0.00660744
[253] valid_0's l1: 0.0569056 valid_0's l2: 0.00660932
[254] valid_0's l1: 0.0569014 valid_0's l2: 0.00660959
[255] valid_0's l1: 0.0568977 valid_0's l2: 0.00660912
[256] valid_0's l1: 0.0568991 valid_0's l2: 0.00660947
[257] valid_0's l1: 0.0568659 valid_0's l2: 0.00660135
[258] valid_0's l1: 0.0568594 valid_0's l2: 0.00660015
[259] valid_0's l1: 0.0568588 valid_0's l2: 0.00659937
[260] valid_0's l1: 0.0568588 valid_0's l2: 0.00659942
[261] valid_0's l1: 0.056858 valid_0's l2: 0.00659948
[262] valid_0's l1: 0.0568521 valid_0's l2: 0.00659886
[263] valid_0's l1: 0.0568516 valid_0's l2: 0.00659866
[264] valid_0's l1: 0.0568237 valid_0's l2: 0.00659403
[265] valid_0's l1: 0.0568185 valid_0's l2: 0.00659247
[266] valid_0's l1: 0.0568207 valid_0's l2: 0.00659355
[267] valid_0's l1: 0.0568153 valid_0's l2: 0.00659291
[268] valid_0's l1: 0.0568104 valid_0's l2: 0.00659252
[269] valid_0's l1: 0.0568035 valid_0's l2: 0.00659123
[270] valid_0's l1: 0.0567872 valid_0's l2: 0.00658465
[271] valid_0's l1: 0.0567721 valid_0's l2: 0.00658252
[272] valid_0's l1: 0.0567615 valid_0's l2: 0.00658002
[273] valid_0's l1: 0.0567417 valid_0's l2: 0.00657648
[274] valid_0's l1: 0.0567421 valid_0's l2: 0.00657556
[275] valid_0's l1: 0.0567452 valid_0's l2: 0.00657642
[276] valid_0's l1: 0.0567469 valid_0's l2: 0.00657607
[277] valid_0's l1: 0.056743 valid_0's l2: 0.00657593
[278] valid_0's l1: 0.0567391 valid_0's l2: 0.00657495
[279] valid_0's l1: 0.056739 valid_0's l2: 0.00657478
[280] valid_0's l1: 0.0567325 valid_0's l2: 0.00657337
[281] valid_0's l1: 0.0567323 valid_0's l2: 0.00657334
[282] valid_0's l1: 0.0567366 valid_0's l2: 0.00657365
[283] valid_0's l1: 0.0567364 valid_0's l2: 0.00657325
[284] valid_0's l1: 0.0567233 valid_0's l2: 0.00657079
[285] valid_0's l1: 0.0567246 valid_0's l2: 0.0065718
[286] valid_0's l1: 0.0567265 valid_0's l2: 0.00657222
[287] valid_0's l1: 0.0567223 valid_0's l2: 0.00657141
[288] valid_0's l1: 0.056714 valid_0's l2: 0.00656963
[289] valid_0's l1: 0.0567092 valid_0's l2: 0.00656967
[290] valid_0's l1: 0.0566989 valid_0's l2: 0.00656775
[291] valid_0's l1: 0.0566969 valid_0's l2: 0.00656714
[292] valid_0's l1: 0.0566972 valid_0's l2: 0.00656797
[293] valid_0's l1: 0.0566986 valid_0's l2: 0.00656812
[294] valid_0's l1: 0.056693 valid_0's l2: 0.00656709
[295] valid_0's l1: 0.0566943 valid_0's l2: 0.00656743
[296] valid_0's l1: 0.0566987 valid_0's l2: 0.0065679
[297] valid_0's l1: 0.056698 valid_0's l2: 0.00656738
[298] valid_0's l1: 0.0566927 valid_0's l2: 0.00656645
[299] valid_0's l1: 0.0566939 valid_0's l2: 0.00656589
[300] valid_0's l1: 0.0566834 valid_0's l2: 0.00656346
Did not meet early stopping. Best iteration is:
[300] valid_0's l1: 0.0566834 valid_0's l2: 0.00656346
本次結果輸出的mae值是:
0.056683443527655
[1] valid_0's l1: 0.244506 valid_0's l2: 0.0790859
Training until validation scores don't improve for 5 rounds
[2] valid_0's l1: 0.223857 valid_0's l2: 0.0667341
[3] valid_0's l1: 0.205626 valid_0's l2: 0.0566941
[4] valid_0's l1: 0.189298 valid_0's l2: 0.0484539
[5] valid_0's l1: 0.175104 valid_0's l2: 0.0417921
[6] valid_0's l1: 0.16227 valid_0's l2: 0.036285
[7] valid_0's l1: 0.150898 valid_0's l2: 0.0317221
[8] valid_0's l1: 0.140883 valid_0's l2: 0.0280196
[9] valid_0's l1: 0.131939 valid_0's l2: 0.0249613
[10] valid_0's l1: 0.124375 valid_0's l2: 0.0224944
[11] valid_0's l1: 0.117467 valid_0's l2: 0.0204263
[12] valid_0's l1: 0.110957 valid_0's l2: 0.0185453
[13] valid_0's l1: 0.105077 valid_0's l2: 0.016929
[14] valid_0's l1: 0.100272 valid_0's l2: 0.0157152
[15] valid_0's l1: 0.0960833 valid_0's l2: 0.0147431
[16] valid_0's l1: 0.0920567 valid_0's l2: 0.0137979
[17] valid_0's l1: 0.088268 valid_0's l2: 0.0129121
[18] valid_0's l1: 0.0854499 valid_0's l2: 0.0123437
[19] valid_0's l1: 0.0827065 valid_0's l2: 0.0117755
[20] valid_0's l1: 0.0800967 valid_0's l2: 0.0112162
[21] valid_0's l1: 0.0781237 valid_0's l2: 0.0108399
[22] valid_0's l1: 0.0763776 valid_0's l2: 0.0105204
[23] valid_0's l1: 0.074906 valid_0's l2: 0.010265
[24] valid_0's l1: 0.0732985 valid_0's l2: 0.00994854
[25] valid_0's l1: 0.0720481 valid_0's l2: 0.00970346
[26] valid_0's l1: 0.0710837 valid_0's l2: 0.00953733
[27] valid_0's l1: 0.0703066 valid_0's l2: 0.00939487
[28] valid_0's l1: 0.0694075 valid_0's l2: 0.009231
[29] valid_0's l1: 0.0685866 valid_0's l2: 0.00907982
[30] valid_0's l1: 0.06779 valid_0's l2: 0.0089391
[31] valid_0's l1: 0.067074 valid_0's l2: 0.00880224
[32] valid_0's l1: 0.0665149 valid_0's l2: 0.00871073
[33] valid_0's l1: 0.0659205 valid_0's l2: 0.00858992
[34] valid_0's l1: 0.0652426 valid_0's l2: 0.00843778
[35] valid_0's l1: 0.0648768 valid_0's l2: 0.00836372
[36] valid_0's l1: 0.0645306 valid_0's l2: 0.00830614
[37] valid_0's l1: 0.0640175 valid_0's l2: 0.00818645
[38] valid_0's l1: 0.0637947 valid_0's l2: 0.00814636
[39] valid_0's l1: 0.0635605 valid_0's l2: 0.00810447
[40] valid_0's l1: 0.0633449 valid_0's l2: 0.00806796
[41] valid_0's l1: 0.0631446 valid_0's l2: 0.00802436
[42] valid_0's l1: 0.0627575 valid_0's l2: 0.00792715
[43] valid_0's l1: 0.062611 valid_0's l2: 0.00789809
[44] valid_0's l1: 0.0623997 valid_0's l2: 0.00785422
[45] valid_0's l1: 0.0622728 valid_0's l2: 0.00783369
[46] valid_0's l1: 0.0621336 valid_0's l2: 0.00781042
[47] valid_0's l1: 0.0619906 valid_0's l2: 0.00777463
[48] valid_0's l1: 0.0618556 valid_0's l2: 0.00774471
[49] valid_0's l1: 0.0617449 valid_0's l2: 0.00772713
[50] valid_0's l1: 0.0616454 valid_0's l2: 0.00770345
[51] valid_0's l1: 0.0615492 valid_0's l2: 0.00768433
[52] valid_0's l1: 0.0614452 valid_0's l2: 0.00766572
[53] valid_0's l1: 0.0613725 valid_0's l2: 0.00764408
[54] valid_0's l1: 0.0612796 valid_0's l2: 0.00762834
[55] valid_0's l1: 0.0611706 valid_0's l2: 0.00760066
[56] valid_0's l1: 0.0611121 valid_0's l2: 0.00759012
[57] valid_0's l1: 0.0610472 valid_0's l2: 0.00757864
[58] valid_0's l1: 0.0609801 valid_0's l2: 0.00756664
[59] valid_0's l1: 0.0608993 valid_0's l2: 0.00755207
[60] valid_0's l1: 0.0608206 valid_0's l2: 0.00753343
[61] valid_0's l1: 0.0607751 valid_0's l2: 0.00752435
[62] valid_0's l1: 0.0607163 valid_0's l2: 0.00751128
[63] valid_0's l1: 0.0605746 valid_0's l2: 0.00747752
[64] valid_0's l1: 0.0604651 valid_0's l2: 0.0074507
[65] valid_0's l1: 0.0604263 valid_0's l2: 0.00744162
[66] valid_0's l1: 0.0603599 valid_0's l2: 0.00742889
[67] valid_0's l1: 0.0602701 valid_0's l2: 0.00741169
[68] valid_0's l1: 0.0602334 valid_0's l2: 0.00739977
[69] valid_0's l1: 0.060112 valid_0's l2: 0.007374
[70] valid_0's l1: 0.0600257 valid_0's l2: 0.00735234
[71] valid_0's l1: 0.059928 valid_0's l2: 0.00732775
[72] valid_0's l1: 0.0598568 valid_0's l2: 0.00731406
[73] valid_0's l1: 0.0598019 valid_0's l2: 0.007307
[74] valid_0's l1: 0.0597175 valid_0's l2: 0.00728248
[75] valid_0's l1: 0.0596246 valid_0's l2: 0.00726385
[76] valid_0's l1: 0.0596029 valid_0's l2: 0.00725546
[77] valid_0's l1: 0.0595231 valid_0's l2: 0.00723235
[78] valid_0's l1: 0.0595014 valid_0's l2: 0.00722831
[79] valid_0's l1: 0.0594712 valid_0's l2: 0.00722155
[80] valid_0's l1: 0.0594512 valid_0's l2: 0.00721585
[81] valid_0's l1: 0.0594197 valid_0's l2: 0.00720856
[82] valid_0's l1: 0.0593932 valid_0's l2: 0.00720475
[83] valid_0's l1: 0.0593157 valid_0's l2: 0.00718559
[84] valid_0's l1: 0.0592926 valid_0's l2: 0.00718174
[85] valid_0's l1: 0.0592627 valid_0's l2: 0.00717558
[86] valid_0's l1: 0.0592466 valid_0's l2: 0.00716984
[87] valid_0's l1: 0.0592043 valid_0's l2: 0.00716193
[88] valid_0's l1: 0.0591298 valid_0's l2: 0.00714342
[89] valid_0's l1: 0.059097 valid_0's l2: 0.00713607
[90] valid_0's l1: 0.0589982 valid_0's l2: 0.00711745
[91] valid_0's l1: 0.0589505 valid_0's l2: 0.00710413
[92] valid_0's l1: 0.0588884 valid_0's l2: 0.00708708
[93] valid_0's l1: 0.0588084 valid_0's l2: 0.00707062
[94] valid_0's l1: 0.0587967 valid_0's l2: 0.00707269
[95] valid_0's l1: 0.0587689 valid_0's l2: 0.00706628
[96] valid_0's l1: 0.058749 valid_0's l2: 0.00705746
[97] valid_0's l1: 0.0587121 valid_0's l2: 0.00704977
[98] valid_0's l1: 0.0586712 valid_0's l2: 0.00703874
[99] valid_0's l1: 0.05861 valid_0's l2: 0.0070237
[100] valid_0's l1: 0.0585889 valid_0's l2: 0.00701449
[101] valid_0's l1: 0.0585771 valid_0's l2: 0.00701003
[102] valid_0's l1: 0.0585635 valid_0's l2: 0.0070058
[103] valid_0's l1: 0.0585479 valid_0's l2: 0.00700123
[104] valid_0's l1: 0.0585384 valid_0's l2: 0.00699934
[105] valid_0's l1: 0.0585209 valid_0's l2: 0.00699689
[106] valid_0's l1: 0.0585063 valid_0's l2: 0.00699365
[107] valid_0's l1: 0.0584905 valid_0's l2: 0.00698833
[108] valid_0's l1: 0.0584738 valid_0's l2: 0.00698361
[109] valid_0's l1: 0.0584563 valid_0's l2: 0.00697852
[110] valid_0's l1: 0.0584379 valid_0's l2: 0.00697721
[111] valid_0's l1: 0.0584333 valid_0's l2: 0.00697385
[112] valid_0's l1: 0.0584316 valid_0's l2: 0.00697371
[113] valid_0's l1: 0.0584076 valid_0's l2: 0.00696515
[114] valid_0's l1: 0.0583903 valid_0's l2: 0.00696145
[115] valid_0's l1: 0.0583817 valid_0's l2: 0.00695705
[116] valid_0's l1: 0.0583585 valid_0's l2: 0.00695171
[117] valid_0's l1: 0.0583329 valid_0's l2: 0.00694439
[118] valid_0's l1: 0.0582953 valid_0's l2: 0.00693451
[119] valid_0's l1: 0.0582302 valid_0's l2: 0.00691471
[120] valid_0's l1: 0.0582053 valid_0's l2: 0.00690717
[121] valid_0's l1: 0.058165 valid_0's l2: 0.00690191
[122] valid_0's l1: 0.0581507 valid_0's l2: 0.00689919
[123] valid_0's l1: 0.0581202 valid_0's l2: 0.00689344
[124] valid_0's l1: 0.0580842 valid_0's l2: 0.00688742
[125] valid_0's l1: 0.0580695 valid_0's l2: 0.00688381
[126] valid_0's l1: 0.0580556 valid_0's l2: 0.0068812
[127] valid_0's l1: 0.0580404 valid_0's l2: 0.00687439
[128] valid_0's l1: 0.0580276 valid_0's l2: 0.00687128
[129] valid_0's l1: 0.0579982 valid_0's l2: 0.00686654
[130] valid_0's l1: 0.0579886 valid_0's l2: 0.00686163
[131] valid_0's l1: 0.0579795 valid_0's l2: 0.00686265
[132] valid_0's l1: 0.0579773 valid_0's l2: 0.00686113
[133] valid_0's l1: 0.0579718 valid_0's l2: 0.00686023
[134] valid_0's l1: 0.0579558 valid_0's l2: 0.00685794
[135] valid_0's l1: 0.0579452 valid_0's l2: 0.00685478
[136] valid_0's l1: 0.0579375 valid_0's l2: 0.00685219
[137] valid_0's l1: 0.0579311 valid_0's l2: 0.00685004
[138] valid_0's l1: 0.0578929 valid_0's l2: 0.0068395
[139] valid_0's l1: 0.0578863 valid_0's l2: 0.00683737
[140] valid_0's l1: 0.0578618 valid_0's l2: 0.00682973
[141] valid_0's l1: 0.057814 valid_0's l2: 0.00682006
[142] valid_0's l1: 0.0577865 valid_0's l2: 0.00681178
[143] valid_0's l1: 0.0577836 valid_0's l2: 0.00681092
[144] valid_0's l1: 0.0577732 valid_0's l2: 0.00680807
[145] valid_0's l1: 0.05775 valid_0's l2: 0.00680476
[146] valid_0's l1: 0.0577295 valid_0's l2: 0.00679983
[147] valid_0's l1: 0.0577274 valid_0's l2: 0.00679906
[148] valid_0's l1: 0.0577282 valid_0's l2: 0.00680071
[149] valid_0's l1: 0.0577027 valid_0's l2: 0.00679154
[150] valid_0's l1: 0.0577023 valid_0's l2: 0.00679031
[151] valid_0's l1: 0.0576895 valid_0's l2: 0.0067869
[152] valid_0's l1: 0.0576863 valid_0's l2: 0.00678669
[153] valid_0's l1: 0.057681 valid_0's l2: 0.00678298
[154] valid_0's l1: 0.0576461 valid_0's l2: 0.00677587
[155] valid_0's l1: 0.0576281 valid_0's l2: 0.00676922
[156] valid_0's l1: 0.0576071 valid_0's l2: 0.00676582
[157] valid_0's l1: 0.0575831 valid_0's l2: 0.00676433
[158] valid_0's l1: 0.0575486 valid_0's l2: 0.00675594
[159] valid_0's l1: 0.0575371 valid_0's l2: 0.00675428
[160] valid_0's l1: 0.0575191 valid_0's l2: 0.00675119
[161] valid_0's l1: 0.0575124 valid_0's l2: 0.00675132
[162] valid_0's l1: 0.0575015 valid_0's l2: 0.00674851
[163] valid_0's l1: 0.0574785 valid_0's l2: 0.00674612
[164] valid_0's l1: 0.0574678 valid_0's l2: 0.00674357
[165] valid_0's l1: 0.0574633 valid_0's l2: 0.00674366
[166] valid_0's l1: 0.0574458 valid_0's l2: 0.00673905
[167] valid_0's l1: 0.0574112 valid_0's l2: 0.00672946
[168] valid_0's l1: 0.0573985 valid_0's l2: 0.00672684
[169] valid_0's l1: 0.0573967 valid_0's l2: 0.00672339
[170] valid_0's l1: 0.0573916 valid_0's l2: 0.00672143
[171] valid_0's l1: 0.0573821 valid_0's l2: 0.00671896
[172] valid_0's l1: 0.0573703 valid_0's l2: 0.00671675
[173] valid_0's l1: 0.0573778 valid_0's l2: 0.00671835
[174] valid_0's l1: 0.0573707 valid_0's l2: 0.00671666
[175] valid_0's l1: 0.0573572 valid_0's l2: 0.00671479
[176] valid_0's l1: 0.0573574 valid_0's l2: 0.0067135
[177] valid_0's l1: 0.0573542 valid_0's l2: 0.00671297
[178] valid_0's l1: 0.057355 valid_0's l2: 0.00671458
[179] valid_0's l1: 0.0573403 valid_0's l2: 0.00671148
[180] valid_0's l1: 0.0573278 valid_0's l2: 0.00670699
[181] valid_0's l1: 0.0573277 valid_0's l2: 0.00670656
[182] valid_0's l1: 0.0573192 valid_0's l2: 0.00670432
[183] valid_0's l1: 0.0573012 valid_0's l2: 0.00670044
[184] valid_0's l1: 0.0573018 valid_0's l2: 0.00669917
[185] valid_0's l1: 0.0572933 valid_0's l2: 0.00669791
[186] valid_0's l1: 0.0572937 valid_0's l2: 0.00669628
[187] valid_0's l1: 0.0572959 valid_0's l2: 0.006696
[188] valid_0's l1: 0.0572946 valid_0's l2: 0.00669529
[189] valid_0's l1: 0.057284 valid_0's l2: 0.00669419
[190] valid_0's l1: 0.0572756 valid_0's l2: 0.00669183
[191] valid_0's l1: 0.0572536 valid_0's l2: 0.00668716
[192] valid_0's l1: 0.0572549 valid_0's l2: 0.0066889
[193] valid_0's l1: 0.0572407 valid_0's l2: 0.00668565
[194] valid_0's l1: 0.0572137 valid_0's l2: 0.00668092
[195] valid_0's l1: 0.0572176 valid_0's l2: 0.00668088
[196] valid_0's l1: 0.0571987 valid_0's l2: 0.00667881
[197] valid_0's l1: 0.0571904 valid_0's l2: 0.00667394
[198] valid_0's l1: 0.0571825 valid_0's l2: 0.00667086
[199] valid_0's l1: 0.0571814 valid_0's l2: 0.0066715
[200] valid_0's l1: 0.0571754 valid_0's l2: 0.0066712
[201] valid_0's l1: 0.0571679 valid_0's l2: 0.00666813
[202] valid_0's l1: 0.0571538 valid_0's l2: 0.00666858
[203] valid_0's l1: 0.0571505 valid_0's l2: 0.00666737
[204] valid_0's l1: 0.0571496 valid_0's l2: 0.00666642
[205] valid_0's l1: 0.0571438 valid_0's l2: 0.00666369
[206] valid_0's l1: 0.0571522 valid_0's l2: 0.0066647
[207] valid_0's l1: 0.0571443 valid_0's l2: 0.00666353
[208] valid_0's l1: 0.057147 valid_0's l2: 0.00666341
[209] valid_0's l1: 0.0571413 valid_0's l2: 0.00666194
[210] valid_0's l1: 0.0571264 valid_0's l2: 0.00666014
[211] valid_0's l1: 0.0571122 valid_0's l2: 0.00665585
[212] valid_0's l1: 0.0571118 valid_0's l2: 0.00665518
[213] valid_0's l1: 0.0571002 valid_0's l2: 0.00665338
[214] valid_0's l1: 0.0570964 valid_0's l2: 0.00665241
[215] valid_0's l1: 0.05709 valid_0's l2: 0.00665055
[216] valid_0's l1: 0.0570856 valid_0's l2: 0.00665057
[217] valid_0's l1: 0.0570826 valid_0's l2: 0.00665004
[218] valid_0's l1: 0.0570747 valid_0's l2: 0.00664898
[219] valid_0's l1: 0.057068 valid_0's l2: 0.00664791
[220] valid_0's l1: 0.05707 valid_0's l2: 0.00664691
[221] valid_0's l1: 0.0570675 valid_0's l2: 0.00664523
[222] valid_0's l1: 0.0570426 valid_0's l2: 0.00664251
[223] valid_0's l1: 0.0570167 valid_0's l2: 0.00663508
[224] valid_0's l1: 0.0570101 valid_0's l2: 0.00663261
[225] valid_0's l1: 0.0570103 valid_0's l2: 0.00663457
[226] valid_0's l1: 0.0569972 valid_0's l2: 0.00663136
[227] valid_0's l1: 0.0569882 valid_0's l2: 0.00663009
[228] valid_0's l1: 0.0569898 valid_0's l2: 0.00662954
[229] valid_0's l1: 0.0569865 valid_0's l2: 0.00662705
[230] valid_0's l1: 0.0569757 valid_0's l2: 0.00662507
[231] valid_0's l1: 0.0569731 valid_0's l2: 0.00662383
[232] valid_0's l1: 0.0569676 valid_0's l2: 0.00662384
[233] valid_0's l1: 0.0569664 valid_0's l2: 0.00662459
[234] valid_0's l1: 0.0569611 valid_0's l2: 0.00662457
[235] valid_0's l1: 0.0569633 valid_0's l2: 0.00662413
[236] valid_0's l1: 0.0569615 valid_0's l2: 0.00662337
[237] valid_0's l1: 0.0569616 valid_0's l2: 0.00662352
[238] valid_0's l1: 0.0569541 valid_0's l2: 0.00662248
[239] valid_0's l1: 0.0569478 valid_0's l2: 0.0066219
[240] valid_0's l1: 0.0569487 valid_0's l2: 0.00662202
[241] valid_0's l1: 0.0569517 valid_0's l2: 0.00662222
[242] valid_0's l1: 0.0569443 valid_0's l2: 0.00662104
[243] valid_0's l1: 0.0569249 valid_0's l2: 0.00661558
[244] valid_0's l1: 0.0569245 valid_0's l2: 0.00661384
[245] valid_0's l1: 0.0569247 valid_0's l2: 0.00661414
[246] valid_0's l1: 0.0569229 valid_0's l2: 0.00661358
[247] valid_0's l1: 0.0569195 valid_0's l2: 0.00661233
[248] valid_0's l1: 0.0569077 valid_0's l2: 0.00660972
[249] valid_0's l1: 0.0569058 valid_0's l2: 0.00660907
[250] valid_0's l1: 0.0569022 valid_0's l2: 0.00660765
[251] valid_0's l1: 0.056903 valid_0's l2: 0.00660776
[252] valid_0's l1: 0.0569011 valid_0's l2: 0.00660744
[253] valid_0's l1: 0.0569056 valid_0's l2: 0.00660932
[254] valid_0's l1: 0.0569014 valid_0's l2: 0.00660959
[255] valid_0's l1: 0.0568977 valid_0's l2: 0.00660912
[256] valid_0's l1: 0.0568991 valid_0's l2: 0.00660947
[257] valid_0's l1: 0.0568659 valid_0's l2: 0.00660135
[258] valid_0's l1: 0.0568594 valid_0's l2: 0.00660015
[259] valid_0's l1: 0.0568588 valid_0's l2: 0.00659937
[260] valid_0's l1: 0.0568588 valid_0's l2: 0.00659942
[261] valid_0's l1: 0.056858 valid_0's l2: 0.00659948
[262] valid_0's l1: 0.0568521 valid_0's l2: 0.00659886
[263] valid_0's l1: 0.0568516 valid_0's l2: 0.00659866
[264] valid_0's l1: 0.0568237 valid_0's l2: 0.00659403
[265] valid_0's l1: 0.0568185 valid_0's l2: 0.00659247
[266] valid_0's l1: 0.0568207 valid_0's l2: 0.00659355
[267] valid_0's l1: 0.0568153 valid_0's l2: 0.00659291
[268] valid_0's l1: 0.0568104 valid_0's l2: 0.00659252
[269] valid_0's l1: 0.0568035 valid_0's l2: 0.00659123
[270] valid_0's l1: 0.0567872 valid_0's l2: 0.00658465
[271] valid_0's l1: 0.0567721 valid_0's l2: 0.00658252
[272] valid_0's l1: 0.0567615 valid_0's l2: 0.00658002
[273] valid_0's l1: 0.0567417 valid_0's l2: 0.00657648
[274] valid_0's l1: 0.0567421 valid_0's l2: 0.00657556
[275] valid_0's l1: 0.0567452 valid_0's l2: 0.00657642
[276] valid_0's l1: 0.0567469 valid_0's l2: 0.00657607
[277] valid_0's l1: 0.056743 valid_0's l2: 0.00657593
[278] valid_0's l1: 0.0567391 valid_0's l2: 0.00657495
[279] valid_0's l1: 0.056739 valid_0's l2: 0.00657478
[280] valid_0's l1: 0.0567325 valid_0's l2: 0.00657337
[281] valid_0's l1: 0.0567323 valid_0's l2: 0.00657334
[282] valid_0's l1: 0.0567366 valid_0's l2: 0.00657365
[283] valid_0's l1: 0.0567364 valid_0's l2: 0.00657325
[284] valid_0's l1: 0.0567233 valid_0's l2: 0.00657079
[285] valid_0's l1: 0.0567246 valid_0's l2: 0.0065718
[286] valid_0's l1: 0.0567265 valid_0's l2: 0.00657222
[287] valid_0's l1: 0.0567223 valid_0's l2: 0.00657141
[288] valid_0's l1: 0.056714 valid_0's l2: 0.00656963
[289] valid_0's l1: 0.0567092 valid_0's l2: 0.00656967
[290] valid_0's l1: 0.0566989 valid_0's l2: 0.00656775
[291] valid_0's l1: 0.0566969 valid_0's l2: 0.00656714
[292] valid_0's l1: 0.0566972 valid_0's l2: 0.00656797
[293] valid_0's l1: 0.0566986 valid_0's l2: 0.00656812
[294] valid_0's l1: 0.056693 valid_0's l2: 0.00656709
[295] valid_0's l1: 0.0566943 valid_0's l2: 0.00656743
[296] valid_0's l1: 0.0566987 valid_0's l2: 0.0065679
[297] valid_0's l1: 0.056698 valid_0's l2: 0.00656738
[298] valid_0's l1: 0.0566927 valid_0's l2: 0.00656645
[299] valid_0's l1: 0.0566939 valid_0's l2: 0.00656589
[300] valid_0's l1: 0.0566834 valid_0's l2: 0.00656346
[301] valid_0's l1: 0.0566872 valid_0's l2: 0.0065663
[302] valid_0's l1: 0.0566856 valid_0's l2: 0.00656487
[303] valid_0's l1: 0.0566822 valid_0's l2: 0.00656261
[304] valid_0's l1: 0.0566848 valid_0's l2: 0.00656279
[305] valid_0's l1: 0.0566818 valid_0's l2: 0.00656087
[306] valid_0's l1: 0.0566818 valid_0's l2: 0.00656023
[307] valid_0's l1: 0.0566825 valid_0's l2: 0.00655957
[308] valid_0's l1: 0.0566804 valid_0's l2: 0.00655989
[309] valid_0's l1: 0.0566693 valid_0's l2: 0.00655513
[310] valid_0's l1: 0.0566608 valid_0's l2: 0.00655397
[311] valid_0's l1: 0.0566546 valid_0's l2: 0.00655309
[312] valid_0's l1: 0.0566519 valid_0's l2: 0.00655274
[313] valid_0's l1: 0.05665 valid_0's l2: 0.00655275
[314] valid_0's l1: 0.0566492 valid_0's l2: 0.00655257
[315] valid_0's l1: 0.0566516 valid_0's l2: 0.00655293
[316] valid_0's l1: 0.0566499 valid_0's l2: 0.00655212
[317] valid_0's l1: 0.0566385 valid_0's l2: 0.00655
[318] valid_0's l1: 0.0566288 valid_0's l2: 0.00654956
[319] valid_0's l1: 0.0566317 valid_0's l2: 0.00655049
[320] valid_0's l1: 0.056631 valid_0's l2: 0.00655062
[321] valid_0's l1: 0.0566272 valid_0's l2: 0.00654966
[322] valid_0's l1: 0.0566247 valid_0's l2: 0.00654857
[323] valid_0's l1: 0.0566318 valid_0's l2: 0.00654932
[324] valid_0's l1: 0.0566344 valid_0's l2: 0.00655044
[325] valid_0's l1: 0.0566239 valid_0's l2: 0.00654858
[326] valid_0's l1: 0.0566241 valid_0's l2: 0.00654811
[327] valid_0's l1: 0.056621 valid_0's l2: 0.00654755
[328] valid_0's l1: 0.056627 valid_0's l2: 0.00655085
[329] valid_0's l1: 0.0566305 valid_0's l2: 0.00655121
[330] valid_0's l1: 0.0566281 valid_0's l2: 0.00655078
[331] valid_0's l1: 0.0566228 valid_0's l2: 0.00655045
[332] valid_0's l1: 0.0566242 valid_0's l2: 0.00654992
Early stopping, best iteration is:
[327] valid_0's l1: 0.056621 valid_0's l2: 0.00654755
本次結果輸出的mae值是:
0.0566209902947507
[1] valid_0's l1: 0.244506 valid_0's l2: 0.0790859
Training until validation scores don't improve for 5 rounds
[2] valid_0's l1: 0.223857 valid_0's l2: 0.0667341
[3] valid_0's l1: 0.205626 valid_0's l2: 0.0566941
[4] valid_0's l1: 0.189298 valid_0's l2: 0.0484539
[5] valid_0's l1: 0.175104 valid_0's l2: 0.0417921
[6] valid_0's l1: 0.16227 valid_0's l2: 0.036285
[7] valid_0's l1: 0.150898 valid_0's l2: 0.0317221
[8] valid_0's l1: 0.140883 valid_0's l2: 0.0280196
[9] valid_0's l1: 0.131939 valid_0's l2: 0.0249613
[10] valid_0's l1: 0.124375 valid_0's l2: 0.0224944
[11] valid_0's l1: 0.117467 valid_0's l2: 0.0204263
[12] valid_0's l1: 0.110957 valid_0's l2: 0.0185453
[13] valid_0's l1: 0.105077 valid_0's l2: 0.016929
[14] valid_0's l1: 0.100272 valid_0's l2: 0.0157152
[15] valid_0's l1: 0.0960833 valid_0's l2: 0.0147431
[16] valid_0's l1: 0.0920567 valid_0's l2: 0.0137979
[17] valid_0's l1: 0.088268 valid_0's l2: 0.0129121
[18] valid_0's l1: 0.0854499 valid_0's l2: 0.0123437
[19] valid_0's l1: 0.0827065 valid_0's l2: 0.0117755
[20] valid_0's l1: 0.0800967 valid_0's l2: 0.0112162
[21] valid_0's l1: 0.0781237 valid_0's l2: 0.0108399
[22] valid_0's l1: 0.0763776 valid_0's l2: 0.0105204
[23] valid_0's l1: 0.074906 valid_0's l2: 0.010265
[24] valid_0's l1: 0.0732985 valid_0's l2: 0.00994854
[25] valid_0's l1: 0.0720481 valid_0's l2: 0.00970346
[26] valid_0's l1: 0.0710837 valid_0's l2: 0.00953733
[27] valid_0's l1: 0.0703066 valid_0's l2: 0.00939487
[28] valid_0's l1: 0.0694075 valid_0's l2: 0.009231
[29] valid_0's l1: 0.0685866 valid_0's l2: 0.00907982
[30] valid_0's l1: 0.06779 valid_0's l2: 0.0089391
[31] valid_0's l1: 0.067074 valid_0's l2: 0.00880224
[32] valid_0's l1: 0.0665149 valid_0's l2: 0.00871073
[33] valid_0's l1: 0.0659205 valid_0's l2: 0.00858992
[34] valid_0's l1: 0.0652426 valid_0's l2: 0.00843778
[35] valid_0's l1: 0.0648768 valid_0's l2: 0.00836372
[36] valid_0's l1: 0.0645306 valid_0's l2: 0.00830614
[37] valid_0's l1: 0.0640175 valid_0's l2: 0.00818645
[38] valid_0's l1: 0.0637947 valid_0's l2: 0.00814636
[39] valid_0's l1: 0.0635605 valid_0's l2: 0.00810447
[40] valid_0's l1: 0.0633449 valid_0's l2: 0.00806796
[41] valid_0's l1: 0.0631446 valid_0's l2: 0.00802436
[42] valid_0's l1: 0.0627575 valid_0's l2: 0.00792715
[43] valid_0's l1: 0.062611 valid_0's l2: 0.00789809
[44] valid_0's l1: 0.0623997 valid_0's l2: 0.00785422
[45] valid_0's l1: 0.0622728 valid_0's l2: 0.00783369
[46] valid_0's l1: 0.0621336 valid_0's l2: 0.00781042
[47] valid_0's l1: 0.0619906 valid_0's l2: 0.00777463
[48] valid_0's l1: 0.0618556 valid_0's l2: 0.00774471
[49] valid_0's l1: 0.0617449 valid_0's l2: 0.00772713
[50] valid_0's l1: 0.0616454 valid_0's l2: 0.00770345
[51] valid_0's l1: 0.0615492 valid_0's l2: 0.00768433
[52] valid_0's l1: 0.0614452 valid_0's l2: 0.00766572
[53] valid_0's l1: 0.0613725 valid_0's l2: 0.00764408
[54] valid_0's l1: 0.0612796 valid_0's l2: 0.00762834
[55] valid_0's l1: 0.0611706 valid_0's l2: 0.00760066
[56] valid_0's l1: 0.0611121 valid_0's l2: 0.00759012
[57] valid_0's l1: 0.0610472 valid_0's l2: 0.00757864
[58] valid_0's l1: 0.0609801 valid_0's l2: 0.00756664
[59] valid_0's l1: 0.0608993 valid_0's l2: 0.00755207
[60] valid_0's l1: 0.0608206 valid_0's l2: 0.00753343
[61] valid_0's l1: 0.0607751 valid_0's l2: 0.00752435
[62] valid_0's l1: 0.0607163 valid_0's l2: 0.00751128
[63] valid_0's l1: 0.0605746 valid_0's l2: 0.00747752
[64] valid_0's l1: 0.0604651 valid_0's l2: 0.0074507
[65] valid_0's l1: 0.0604263 valid_0's l2: 0.00744162
[66] valid_0's l1: 0.0603599 valid_0's l2: 0.00742889
[67] valid_0's l1: 0.0602701 valid_0's l2: 0.00741169
[68] valid_0's l1: 0.0602334 valid_0's l2: 0.00739977
[69] valid_0's l1: 0.060112 valid_0's l2: 0.007374
[70] valid_0's l1: 0.0600257 valid_0's l2: 0.00735234
[71] valid_0's l1: 0.059928 valid_0's l2: 0.00732775
[72] valid_0's l1: 0.0598568 valid_0's l2: 0.00731406
[73] valid_0's l1: 0.0598019 valid_0's l2: 0.007307
[74] valid_0's l1: 0.0597175 valid_0's l2: 0.00728248
[75] valid_0's l1: 0.0596246 valid_0's l2: 0.00726385
[76] valid_0's l1: 0.0596029 valid_0's l2: 0.00725546
[77] valid_0's l1: 0.0595231 valid_0's l2: 0.00723235
[78] valid_0's l1: 0.0595014 valid_0's l2: 0.00722831
[79] valid_0's l1: 0.0594712 valid_0's l2: 0.00722155
[80] valid_0's l1: 0.0594512 valid_0's l2: 0.00721585
[81] valid_0's l1: 0.0594197 valid_0's l2: 0.00720856
[82] valid_0's l1: 0.0593932 valid_0's l2: 0.00720475
[83] valid_0's l1: 0.0593157 valid_0's l2: 0.00718559
[84] valid_0's l1: 0.0592926 valid_0's l2: 0.00718174
[85] valid_0's l1: 0.0592627 valid_0's l2: 0.00717558
[86] valid_0's l1: 0.0592466 valid_0's l2: 0.00716984
[87] valid_0's l1: 0.0592043 valid_0's l2: 0.00716193
[88] valid_0's l1: 0.0591298 valid_0's l2: 0.00714342
[89] valid_0's l1: 0.059097 valid_0's l2: 0.00713607
[90] valid_0's l1: 0.0589982 valid_0's l2: 0.00711745
[91] valid_0's l1: 0.0589505 valid_0's l2: 0.00710413
[92] valid_0's l1: 0.0588884 valid_0's l2: 0.00708708
[93] valid_0's l1: 0.0588084 valid_0's l2: 0.00707062
[94] valid_0's l1: 0.0587967 valid_0's l2: 0.00707269
[95] valid_0's l1: 0.0587689 valid_0's l2: 0.00706628
[96] valid_0's l1: 0.058749 valid_0's l2: 0.00705746
[97] valid_0's l1: 0.0587121 valid_0's l2: 0.00704977
[98] valid_0's l1: 0.0586712 valid_0's l2: 0.00703874
[99] valid_0's l1: 0.05861 valid_0's l2: 0.0070237
[100] valid_0's l1: 0.0585889 valid_0's l2: 0.00701449
[101] valid_0's l1: 0.0585771 valid_0's l2: 0.00701003
[102] valid_0's l1: 0.0585635 valid_0's l2: 0.0070058
[103] valid_0's l1: 0.0585479 valid_0's l2: 0.00700123
[104] valid_0's l1: 0.0585384 valid_0's l2: 0.00699934
[105] valid_0's l1: 0.0585209 valid_0's l2: 0.00699689
[106] valid_0's l1: 0.0585063 valid_0's l2: 0.00699365
[107] valid_0's l1: 0.0584905 valid_0's l2: 0.00698833
[108] valid_0's l1: 0.0584738 valid_0's l2: 0.00698361
[109] valid_0's l1: 0.0584563 valid_0's l2: 0.00697852
[110] valid_0's l1: 0.0584379 valid_0's l2: 0.00697721
[111] valid_0's l1: 0.0584333 valid_0's l2: 0.00697385
[112] valid_0's l1: 0.0584316 valid_0's l2: 0.00697371
[113] valid_0's l1: 0.0584076 valid_0's l2: 0.00696515
[114] valid_0's l1: 0.0583903 valid_0's l2: 0.00696145
[115] valid_0's l1: 0.0583817 valid_0's l2: 0.00695705
[116] valid_0's l1: 0.0583585 valid_0's l2: 0.00695171
[117] valid_0's l1: 0.0583329 valid_0's l2: 0.00694439
[118] valid_0's l1: 0.0582953 valid_0's l2: 0.00693451
[119] valid_0's l1: 0.0582302 valid_0's l2: 0.00691471
[120] valid_0's l1: 0.0582053 valid_0's l2: 0.00690717
[121] valid_0's l1: 0.058165 valid_0's l2: 0.00690191
[122] valid_0's l1: 0.0581507 valid_0's l2: 0.00689919
[123] valid_0's l1: 0.0581202 valid_0's l2: 0.00689344
[124] valid_0's l1: 0.0580842 valid_0's l2: 0.00688742
[125] valid_0's l1: 0.0580695 valid_0's l2: 0.00688381
[126] valid_0's l1: 0.0580556 valid_0's l2: 0.0068812
[127] valid_0's l1: 0.0580404 valid_0's l2: 0.00687439
[128] valid_0's l1: 0.0580276 valid_0's l2: 0.00687128
[129] valid_0's l1: 0.0579982 valid_0's l2: 0.00686654
[130] valid_0's l1: 0.0579886 valid_0's l2: 0.00686163
[131] valid_0's l1: 0.0579795 valid_0's l2: 0.00686265
[132] valid_0's l1: 0.0579773 valid_0's l2: 0.00686113
[133] valid_0's l1: 0.0579718 valid_0's l2: 0.00686023
[134] valid_0's l1: 0.0579558 valid_0's l2: 0.00685794
[135] valid_0's l1: 0.0579452 valid_0's l2: 0.00685478
[136] valid_0's l1: 0.0579375 valid_0's l2: 0.00685219
[137] valid_0's l1: 0.0579311 valid_0's l2: 0.00685004
[138] valid_0's l1: 0.0578929 valid_0's l2: 0.0068395
[139] valid_0's l1: 0.0578863 valid_0's l2: 0.00683737
[140] valid_0's l1: 0.0578618 valid_0's l2: 0.00682973
[141] valid_0's l1: 0.057814 valid_0's l2: 0.00682006
[142] valid_0's l1: 0.0577865 valid_0's l2: 0.00681178
[143] valid_0's l1: 0.0577836 valid_0's l2: 0.00681092
[144] valid_0's l1: 0.0577732 valid_0's l2: 0.00680807
[145] valid_0's l1: 0.05775 valid_0's l2: 0.00680476
[146] valid_0's l1: 0.0577295 valid_0's l2: 0.00679983
[147] valid_0's l1: 0.0577274 valid_0's l2: 0.00679906
[148] valid_0's l1: 0.0577282 valid_0's l2: 0.00680071
[149] valid_0's l1: 0.0577027 valid_0's l2: 0.00679154
[150] valid_0's l1: 0.0577023 valid_0's l2: 0.00679031
[151] valid_0's l1: 0.0576895 valid_0's l2: 0.0067869
[152] valid_0's l1: 0.0576863 valid_0's l2: 0.00678669
[153] valid_0's l1: 0.057681 valid_0's l2: 0.00678298
[154] valid_0's l1: 0.0576461 valid_0's l2: 0.00677587
[155] valid_0's l1: 0.0576281 valid_0's l2: 0.00676922
[156] valid_0's l1: 0.0576071 valid_0's l2: 0.00676582
[157] valid_0's l1: 0.0575831 valid_0's l2: 0.00676433
[158] valid_0's l1: 0.0575486 valid_0's l2: 0.00675594
[159] valid_0's l1: 0.0575371 valid_0's l2: 0.00675428
[160] valid_0's l1: 0.0575191 valid_0's l2: 0.00675119
[161] valid_0's l1: 0.0575124 valid_0's l2: 0.00675132
[162] valid_0's l1: 0.0575015 valid_0's l2: 0.00674851
[163] valid_0's l1: 0.0574785 valid_0's l2: 0.00674612
[164] valid_0's l1: 0.0574678 valid_0's l2: 0.00674357
[165] valid_0's l1: 0.0574633 valid_0's l2: 0.00674366
[166] valid_0's l1: 0.0574458 valid_0's l2: 0.00673905
[167] valid_0's l1: 0.0574112 valid_0's l2: 0.00672946
[168] valid_0's l1: 0.0573985 valid_0's l2: 0.00672684
[169] valid_0's l1: 0.0573967 valid_0's l2: 0.00672339
[170] valid_0's l1: 0.0573916 valid_0's l2: 0.00672143
[171] valid_0's l1: 0.0573821 valid_0's l2: 0.00671896
[172] valid_0's l1: 0.0573703 valid_0's l2: 0.00671675
[173] valid_0's l1: 0.0573778 valid_0's l2: 0.00671835
[174] valid_0's l1: 0.0573707 valid_0's l2: 0.00671666
[175] valid_0's l1: 0.0573572 valid_0's l2: 0.00671479
[176] valid_0's l1: 0.0573574 valid_0's l2: 0.0067135
[177] valid_0's l1: 0.0573542 valid_0's l2: 0.00671297
[178] valid_0's l1: 0.057355 valid_0's l2: 0.00671458
[179] valid_0's l1: 0.0573403 valid_0's l2: 0.00671148
[180] valid_0's l1: 0.0573278 valid_0's l2: 0.00670699
[181] valid_0's l1: 0.0573277 valid_0's l2: 0.00670656
[182] valid_0's l1: 0.0573192 valid_0's l2: 0.00670432
[183] valid_0's l1: 0.0573012 valid_0's l2: 0.00670044
[184] valid_0's l1: 0.0573018 valid_0's l2: 0.00669917
[185] valid_0's l1: 0.0572933 valid_0's l2: 0.00669791
[186] valid_0's l1: 0.0572937 valid_0's l2: 0.00669628
[187] valid_0's l1: 0.0572959 valid_0's l2: 0.006696
[188] valid_0's l1: 0.0572946 valid_0's l2: 0.00669529
[189] valid_0's l1: 0.057284 valid_0's l2: 0.00669419
[190] valid_0's l1: 0.0572756 valid_0's l2: 0.00669183
[191] valid_0's l1: 0.0572536 valid_0's l2: 0.00668716
[192] valid_0's l1: 0.0572549 valid_0's l2: 0.0066889
[193] valid_0's l1: 0.0572407 valid_0's l2: 0.00668565
[194] valid_0's l1: 0.0572137 valid_0's l2: 0.00668092
[195] valid_0's l1: 0.0572176 valid_0's l2: 0.00668088
[196] valid_0's l1: 0.0571987 valid_0's l2: 0.00667881
[197] valid_0's l1: 0.0571904 valid_0's l2: 0.00667394
[198] valid_0's l1: 0.0571825 valid_0's l2: 0.00667086
[199] valid_0's l1: 0.0571814 valid_0's l2: 0.0066715
[200] valid_0's l1: 0.0571754 valid_0's l2: 0.0066712
[201] valid_0's l1: 0.0571679 valid_0's l2: 0.00666813
[202] valid_0's l1: 0.0571538 valid_0's l2: 0.00666858
[203] valid_0's l1: 0.0571505 valid_0's l2: 0.00666737
[204] valid_0's l1: 0.0571496 valid_0's l2: 0.00666642
[205] valid_0's l1: 0.0571438 valid_0's l2: 0.00666369
[206] valid_0's l1: 0.0571522 valid_0's l2: 0.0066647
[207] valid_0's l1: 0.0571443 valid_0's l2: 0.00666353
[208] valid_0's l1: 0.057147 valid_0's l2: 0.00666341
[209] valid_0's l1: 0.0571413 valid_0's l2: 0.00666194
[210] valid_0's l1: 0.0571264 valid_0's l2: 0.00666014
[211] valid_0's l1: 0.0571122 valid_0's l2: 0.00665585
[212] valid_0's l1: 0.0571118 valid_0's l2: 0.00665518
[213] valid_0's l1: 0.0571002 valid_0's l2: 0.00665338
[214] valid_0's l1: 0.0570964 valid_0's l2: 0.00665241
[215] valid_0's l1: 0.05709 valid_0's l2: 0.00665055
[216] valid_0's l1: 0.0570856 valid_0's l2: 0.00665057
[217] valid_0's l1: 0.0570826 valid_0's l2: 0.00665004
[218] valid_0's l1: 0.0570747 valid_0's l2: 0.00664898
[219] valid_0's l1: 0.057068 valid_0's l2: 0.00664791
[220] valid_0's l1: 0.05707 valid_0's l2: 0.00664691
[221] valid_0's l1: 0.0570675 valid_0's l2: 0.00664523
[222] valid_0's l1: 0.0570426 valid_0's l2: 0.00664251
[223] valid_0's l1: 0.0570167 valid_0's l2: 0.00663508
[224] valid_0's l1: 0.0570101 valid_0's l2: 0.00663261
[225] valid_0's l1: 0.0570103 valid_0's l2: 0.00663457
[226] valid_0's l1: 0.0569972 valid_0's l2: 0.00663136
[227] valid_0's l1: 0.0569882 valid_0's l2: 0.00663009
[228] valid_0's l1: 0.0569898 valid_0's l2: 0.00662954
[229] valid_0's l1: 0.0569865 valid_0's l2: 0.00662705
[230] valid_0's l1: 0.0569757 valid_0's l2: 0.00662507
[231] valid_0's l1: 0.0569731 valid_0's l2: 0.00662383
[232] valid_0's l1: 0.0569676 valid_0's l2: 0.00662384
[233] valid_0's l1: 0.0569664 valid_0's l2: 0.00662459
[234] valid_0's l1: 0.0569611 valid_0's l2: 0.00662457
[235] valid_0's l1: 0.0569633 valid_0's l2: 0.00662413
[236] valid_0's l1: 0.0569615 valid_0's l2: 0.00662337
[237] valid_0's l1: 0.0569616 valid_0's l2: 0.00662352
[238] valid_0's l1: 0.0569541 valid_0's l2: 0.00662248
[239] valid_0's l1: 0.0569478 valid_0's l2: 0.0066219
[240] valid_0's l1: 0.0569487 valid_0's l2: 0.00662202
[241] valid_0's l1: 0.0569517 valid_0's l2: 0.00662222
[242] valid_0's l1: 0.0569443 valid_0's l2: 0.00662104
[243] valid_0's l1: 0.0569249 valid_0's l2: 0.00661558
[244] valid_0's l1: 0.0569245 valid_0's l2: 0.00661384
[245] valid_0's l1: 0.0569247 valid_0's l2: 0.00661414
[246] valid_0's l1: 0.0569229 valid_0's l2: 0.00661358
[247] valid_0's l1: 0.0569195 valid_0's l2: 0.00661233
[248] valid_0's l1: 0.0569077 valid_0's l2: 0.00660972
[249] valid_0's l1: 0.0569058 valid_0's l2: 0.00660907
[250] valid_0's l1: 0.0569022 valid_0's l2: 0.00660765
[251] valid_0's l1: 0.056903 valid_0's l2: 0.00660776
[252] valid_0's l1: 0.0569011 valid_0's l2: 0.00660744
[253] valid_0's l1: 0.0569056 valid_0's l2: 0.00660932
[254] valid_0's l1: 0.0569014 valid_0's l2: 0.00660959
[255] valid_0's l1: 0.0568977 valid_0's l2: 0.00660912
[256] valid_0's l1: 0.0568991 valid_0's l2: 0.00660947
[257] valid_0's l1: 0.0568659 valid_0's l2: 0.00660135
[258] valid_0's l1: 0.0568594 valid_0's l2: 0.00660015
[259] valid_0's l1: 0.0568588 valid_0's l2: 0.00659937
[260] valid_0's l1: 0.0568588 valid_0's l2: 0.00659942
[261] valid_0's l1: 0.056858 valid_0's l2: 0.00659948
[262] valid_0's l1: 0.0568521 valid_0's l2: 0.00659886
[263] valid_0's l1: 0.0568516 valid_0's l2: 0.00659866
[264] valid_0's l1: 0.0568237 valid_0's l2: 0.00659403
[265] valid_0's l1: 0.0568185 valid_0's l2: 0.00659247
[266] valid_0's l1: 0.0568207 valid_0's l2: 0.00659355
[267] valid_0's l1: 0.0568153 valid_0's l2: 0.00659291
[268] valid_0's l1: 0.0568104 valid_0's l2: 0.00659252
[269] valid_0's l1: 0.0568035 valid_0's l2: 0.00659123
[270] valid_0's l1: 0.0567872 valid_0's l2: 0.00658465
[271] valid_0's l1: 0.0567721 valid_0's l2: 0.00658252
[272] valid_0's l1: 0.0567615 valid_0's l2: 0.00658002
[273] valid_0's l1: 0.0567417 valid_0's l2: 0.00657648
[274] valid_0's l1: 0.0567421 valid_0's l2: 0.00657556
[275] valid_0's l1: 0.0567452 valid_0's l2: 0.00657642
[276] valid_0's l1: 0.0567469 valid_0's l2: 0.00657607
[277] valid_0's l1: 0.056743 valid_0's l2: 0.00657593
[278] valid_0's l1: 0.0567391 valid_0's l2: 0.00657495
[279] valid_0's l1: 0.056739 valid_0's l2: 0.00657478
[280] valid_0's l1: 0.0567325 valid_0's l2: 0.00657337
[281] valid_0's l1: 0.0567323 valid_0's l2: 0.00657334
[282] valid_0's l1: 0.0567366 valid_0's l2: 0.00657365
[283] valid_0's l1: 0.0567364 valid_0's l2: 0.00657325
[284] valid_0's l1: 0.0567233 valid_0's l2: 0.00657079
[285] valid_0's l1: 0.0567246 valid_0's l2: 0.0065718
[286] valid_0's l1: 0.0567265 valid_0's l2: 0.00657222
[287] valid_0's l1: 0.0567223 valid_0's l2: 0.00657141
[288] valid_0's l1: 0.056714 valid_0's l2: 0.00656963
[289] valid_0's l1: 0.0567092 valid_0's l2: 0.00656967
[290] valid_0's l1: 0.0566989 valid_0's l2: 0.00656775
[291] valid_0's l1: 0.0566969 valid_0's l2: 0.00656714
[292] valid_0's l1: 0.0566972 valid_0's l2: 0.00656797
[293] valid_0's l1: 0.0566986 valid_0's l2: 0.00656812
[294] valid_0's l1: 0.056693 valid_0's l2: 0.00656709
[295] valid_0's l1: 0.0566943 valid_0's l2: 0.00656743
[296] valid_0's l1: 0.0566987 valid_0's l2: 0.0065679
[297] valid_0's l1: 0.056698 valid_0's l2: 0.00656738
[298] valid_0's l1: 0.0566927 valid_0's l2: 0.00656645
[299] valid_0's l1: 0.0566939 valid_0's l2: 0.00656589
[300] valid_0's l1: 0.0566834 valid_0's l2: 0.00656346
[301] valid_0's l1: 0.0566872 valid_0's l2: 0.0065663
[302] valid_0's l1: 0.0566856 valid_0's l2: 0.00656487
[303] valid_0's l1: 0.0566822 valid_0's l2: 0.00656261
[304] valid_0's l1: 0.0566848 valid_0's l2: 0.00656279
[305] valid_0's l1: 0.0566818 valid_0's l2: 0.00656087
[306] valid_0's l1: 0.0566818 valid_0's l2: 0.00656023
[307] valid_0's l1: 0.0566825 valid_0's l2: 0.00655957
[308] valid_0's l1: 0.0566804 valid_0's l2: 0.00655989
[309] valid_0's l1: 0.0566693 valid_0's l2: 0.00655513
[310] valid_0's l1: 0.0566608 valid_0's l2: 0.00655397
[311] valid_0's l1: 0.0566546 valid_0's l2: 0.00655309
[312] valid_0's l1: 0.0566519 valid_0's l2: 0.00655274
[313] valid_0's l1: 0.05665 valid_0's l2: 0.00655275
[314] valid_0's l1: 0.0566492 valid_0's l2: 0.00655257
[315] valid_0's l1: 0.0566516 valid_0's l2: 0.00655293
[316] valid_0's l1: 0.0566499 valid_0's l2: 0.00655212
[317] valid_0's l1: 0.0566385 valid_0's l2: 0.00655
[318] valid_0's l1: 0.0566288 valid_0's l2: 0.00654956
[319] valid_0's l1: 0.0566317 valid_0's l2: 0.00655049
[320] valid_0's l1: 0.056631 valid_0's l2: 0.00655062
[321] valid_0's l1: 0.0566272 valid_0's l2: 0.00654966
[322] valid_0's l1: 0.0566247 valid_0's l2: 0.00654857
[323] valid_0's l1: 0.0566318 valid_0's l2: 0.00654932
[324] valid_0's l1: 0.0566344 valid_0's l2: 0.00655044
[325] valid_0's l1: 0.0566239 valid_0's l2: 0.00654858
[326] valid_0's l1: 0.0566241 valid_0's l2: 0.00654811
[327] valid_0's l1: 0.056621 valid_0's l2: 0.00654755
[328] valid_0's l1: 0.056627 valid_0's l2: 0.00655085
[329] valid_0's l1: 0.0566305 valid_0's l2: 0.00655121
[330] valid_0's l1: 0.0566281 valid_0's l2: 0.00655078
[331] valid_0's l1: 0.0566228 valid_0's l2: 0.00655045
[332] valid_0's l1: 0.0566242 valid_0's l2: 0.00654992
Early stopping, best iteration is:
[327] valid_0's l1: 0.056621 valid_0's l2: 0.00654755
本次結果輸出的mae值是:
0.0566209902947507
In [174]:
plt.plot(n_estimators,scores,'o-')
plt.ylabel("mae")
plt.xlabel("n_estimator")
print("best n_estimator {}".format(n_estimators[np.argmin(scores)]))
best n_estimator 500
In [175]:
# max_depth
scores = []
max_depth = [3, 5, 7, 9, 11]
for md in max_depth:
lgbm = lgb.LGBMRegressor(boosting_type='gbdt',
num_leaves=31,
max_depth=md,
learning_rate=0.1,
n_estimators=500,
min_child_samples=20,
n_jobs=-1)
lgbm.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], eval_metric="l1", early_stopping_rounds=5)
y_pre = lgbm.predict(X_valid)
mae = mean_absolute_error(y_valid, y_pre)
scores.append(mae)
print("本次結果輸出的mae值是:\n", mae)
[1] valid_0's l1: 0.245951 valid_0's l2: 0.0799026
Training until validation scores don't improve for 5 rounds
[2] valid_0's l1: 0.226678 valid_0's l2: 0.0682131
[3] valid_0's l1: 0.209595 valid_0's l2: 0.0586855
[4] valid_0's l1: 0.194338 valid_0's l2: 0.0508607
[5] valid_0's l1: 0.181069 valid_0's l2: 0.0445219
[6] valid_0's l1: 0.169189 valid_0's l2: 0.0392848
[7] valid_0's l1: 0.158873 valid_0's l2: 0.0350431
[8] valid_0's l1: 0.149769 valid_0's l2: 0.031557
[9] valid_0's l1: 0.141415 valid_0's l2: 0.0285897
[10] valid_0's l1: 0.134067 valid_0's l2: 0.0261583
[11] valid_0's l1: 0.127481 valid_0's l2: 0.0240992
[12] valid_0's l1: 0.121933 valid_0's l2: 0.0224835
[13] valid_0's l1: 0.116908 valid_0's l2: 0.0210707
[14] valid_0's l1: 0.112637 valid_0's l2: 0.0199038
[15] valid_0's l1: 0.108706 valid_0's l2: 0.0188823
[16] valid_0's l1: 0.105431 valid_0's l2: 0.0180432
[17] valid_0's l1: 0.102307 valid_0's l2: 0.0172914
[18] valid_0's l1: 0.0996523 valid_0's l2: 0.0166384
[19] valid_0's l1: 0.0972029 valid_0's l2: 0.0160783
[20] valid_0's l1: 0.0950796 valid_0's l2: 0.0156012
[21] valid_0's l1: 0.0928554 valid_0's l2: 0.0150882
[22] valid_0's l1: 0.0912364 valid_0's l2: 0.0147276
[23] valid_0's l1: 0.0893773 valid_0's l2: 0.0143482
[24] valid_0's l1: 0.087748 valid_0's l2: 0.0139913
[25] valid_0's l1: 0.0867009 valid_0's l2: 0.0137646
[26] valid_0's l1: 0.0855458 valid_0's l2: 0.0135216
[27] valid_0's l1: 0.0845268 valid_0's l2: 0.0132951
[28] valid_0's l1: 0.0834586 valid_0's l2: 0.01308
[29] valid_0's l1: 0.0820494 valid_0's l2: 0.0126823
[30] valid_0's l1: 0.0810451 valid_0's l2: 0.0124618
[31] valid_0's l1: 0.0802479 valid_0's l2: 0.0122846
[32] valid_0's l1: 0.0796107 valid_0's l2: 0.0121644
[33] valid_0's l1: 0.0790837 valid_0's l2: 0.0120704
[34] valid_0's l1: 0.0784538 valid_0's l2: 0.0119457
[35] valid_0's l1: 0.0780126 valid_0's l2: 0.0118636
[36] valid_0's l1: 0.077716 valid_0's l2: 0.0117917
[37] valid_0's l1: 0.077197 valid_0's l2: 0.0116888
[38] valid_0's l1: 0.0768649 valid_0's l2: 0.0116143
[39] valid_0's l1: 0.0760565 valid_0's l2: 0.0114137
[40] valid_0's l1: 0.0752823 valid_0's l2: 0.0112145
[41] valid_0's l1: 0.0749631 valid_0's l2: 0.0111483
[42] valid_0's l1: 0.0746633 valid_0's l2: 0.0110827
[43] valid_0's l1: 0.0744063 valid_0's l2: 0.011016
[44] valid_0's l1: 0.074134 valid_0's l2: 0.0109585
[45] valid_0's l1: 0.0738961 valid_0's l2: 0.010913
[46] valid_0's l1: 0.0737403 valid_0's l2: 0.0108762
[47] valid_0's l1: 0.0731932 valid_0's l2: 0.0107357
[48] valid_0's l1: 0.0729765 valid_0's l2: 0.0106948
[49] valid_0's l1: 0.0728205 valid_0's l2: 0.0106548
[50] valid_0's l1: 0.0722648 valid_0's l2: 0.0105136
[51] valid_0's l1: 0.0721261 valid_0's l2: 0.0104765
[52] valid_0's l1: 0.071956 valid_0's l2: 0.0104392
[53] valid_0's l1: 0.0717767 valid_0's l2: 0.0104094
[54] valid_0's l1: 0.0716089 valid_0's l2: 0.0103866
[55] valid_0's l1: 0.071301 valid_0's l2: 0.0103142
[56] valid_0's l1: 0.0711698 valid_0's l2: 0.0102875
[57] valid_0's l1: 0.0710264 valid_0's l2: 0.0102616
[58] valid_0's l1: 0.0708755 valid_0's l2: 0.0102362
[59] valid_0's l1: 0.0707723 valid_0's l2: 0.0102076
[60] valid_0's l1: 0.0706688 valid_0's l2: 0.0101851
[61] valid_0's l1: 0.0705466 valid_0's l2: 0.0101611
[62] valid_0's l1: 0.070417 valid_0's l2: 0.0101418
[63] valid_0's l1: 0.0703099 valid_0's l2: 0.0101184
[64] valid_0's l1: 0.070014 valid_0's l2: 0.0100408
[65] valid_0's l1: 0.0698032 valid_0's l2: 0.00998559
[66] valid_0's l1: 0.0695586 valid_0's l2: 0.00992395
[67] valid_0's l1: 0.0694659 valid_0's l2: 0.00991016
[68] valid_0's l1: 0.0693805 valid_0's l2: 0.00988416
[69] valid_0's l1: 0.0692109 valid_0's l2: 0.00983863
[70] valid_0's l1: 0.0691033 valid_0's l2: 0.00981832
[71] valid_0's l1: 0.0688559 valid_0's l2: 0.00974502
[72] valid_0's l1: 0.0687006 valid_0's l2: 0.00970238
[73] valid_0's l1: 0.06848 valid_0's l2: 0.0096301
[74] valid_0's l1: 0.0684223 valid_0's l2: 0.00961455
[75] valid_0's l1: 0.0683508 valid_0's l2: 0.00958615
[76] valid_0's l1: 0.0683052 valid_0's l2: 0.009572
[77] valid_0's l1: 0.0681153 valid_0's l2: 0.00952969
[78] valid_0's l1: 0.0680296 valid_0's l2: 0.00951449
[79] valid_0's l1: 0.0678274 valid_0's l2: 0.00944451
[80] valid_0's l1: 0.0677938 valid_0's l2: 0.00943237
[81] valid_0's l1: 0.0676954 valid_0's l2: 0.00940965
[82] valid_0's l1: 0.0676028 valid_0's l2: 0.0093877
[83] valid_0's l1: 0.0674349 valid_0's l2: 0.0093459
[84] valid_0's l1: 0.0673912 valid_0's l2: 0.00932776
[85] valid_0's l1: 0.0673384 valid_0's l2: 0.00931617
[86] valid_0's l1: 0.0672796 valid_0's l2: 0.00930222
[87] valid_0's l1: 0.0672013 valid_0's l2: 0.00928555
[88] valid_0's l1: 0.067058 valid_0's l2: 0.00923902
[89] valid_0's l1: 0.0669635 valid_0's l2: 0.0092177
[90] valid_0's l1: 0.0668587 valid_0's l2: 0.0091912
[91] valid_0's l1: 0.0667102 valid_0's l2: 0.00913804
[92] valid_0's l1: 0.0666526 valid_0's l2: 0.00911653
[93] valid_0's l1: 0.0666176 valid_0's l2: 0.0091075
[94] valid_0's l1: 0.0665381 valid_0's l2: 0.00908938
[95] valid_0's l1: 0.0664094 valid_0's l2: 0.00905902
[96] valid_0's l1: 0.06634 valid_0's l2: 0.00903684
[97] valid_0's l1: 0.0662206 valid_0's l2: 0.00899158
[98] valid_0's l1: 0.0661688 valid_0's l2: 0.0089855
[99] valid_0's l1: 0.0660773 valid_0's l2: 0.00896682
[100] valid_0's l1: 0.0659306 valid_0's l2: 0.00892838
[101] valid_0's l1: 0.0658745 valid_0's l2: 0.0089192
[102] valid_0's l1: 0.065791 valid_0's l2: 0.00888833
[103] valid_0's l1: 0.0656774 valid_0's l2: 0.00885243
[104] valid_0's l1: 0.0655935 valid_0's l2: 0.00882508
[105] valid_0's l1: 0.0655746 valid_0's l2: 0.00881604
[106] valid_0's l1: 0.0655173 valid_0's l2: 0.00880362
[107] valid_0's l1: 0.0653877 valid_0's l2: 0.00877138
[108] valid_0's l1: 0.065285 valid_0's l2: 0.00874944
[109] valid_0's l1: 0.0652656 valid_0's l2: 0.00874508
[110] valid_0's l1: 0.0652144 valid_0's l2: 0.00873215
[111] valid_0's l1: 0.0650925 valid_0's l2: 0.00870511
[112] valid_0's l1: 0.0650302 valid_0's l2: 0.00867355
[113] valid_0's l1: 0.064989 valid_0's l2: 0.00866171
[114] valid_0's l1: 0.0649491 valid_0's l2: 0.00864751
[115] valid_0's l1: 0.0649154 valid_0's l2: 0.00864013
[116] valid_0's l1: 0.0647886 valid_0's l2: 0.00860299
[117] valid_0's l1: 0.0647248 valid_0's l2: 0.00859226
[118] valid_0's l1: 0.0646513 valid_0's l2: 0.00856838
[119] valid_0's l1: 0.0645208 valid_0's l2: 0.00853253
[120] valid_0's l1: 0.0644689 valid_0's l2: 0.00852473
[121] valid_0's l1: 0.0643448 valid_0's l2: 0.00849375
[122] valid_0's l1: 0.064289 valid_0's l2: 0.00847424
[123] valid_0's l1: 0.0642224 valid_0's l2: 0.00844912
[124] valid_0's l1: 0.0642043 valid_0's l2: 0.0084414
[125] valid_0's l1: 0.0641053 valid_0's l2: 0.00841524
[126] valid_0's l1: 0.0640266 valid_0's l2: 0.00838405
[127] valid_0's l1: 0.0639821 valid_0's l2: 0.00837852
[128] valid_0's l1: 0.0639547 valid_0's l2: 0.0083617
[129] valid_0's l1: 0.0639358 valid_0's l2: 0.00835842
[130] valid_0's l1: 0.0638841 valid_0's l2: 0.00834969
[131] valid_0's l1: 0.0638538 valid_0's l2: 0.0083397
[132] valid_0's l1: 0.063819 valid_0's l2: 0.00833278
[133] valid_0's l1: 0.0637716 valid_0's l2: 0.00832131
[134] valid_0's l1: 0.0637488 valid_0's l2: 0.0083152
[135] valid_0's l1: 0.0637281 valid_0's l2: 0.0083078
[136] valid_0's l1: 0.0636638 valid_0's l2: 0.00828725
[137] valid_0's l1: 0.0636322 valid_0's l2: 0.00828142
[138] valid_0's l1: 0.0635598 valid_0's l2: 0.00826705
[139] valid_0's l1: 0.0635517 valid_0's l2: 0.00826326
[140] valid_0's l1: 0.0635109 valid_0's l2: 0.00825556
[141] valid_0's l1: 0.0634626 valid_0's l2: 0.0082434
[142] valid_0's l1: 0.063427 valid_0's l2: 0.0082356
[143] valid_0's l1: 0.0633992 valid_0's l2: 0.00823178
[144] valid_0's l1: 0.0633714 valid_0's l2: 0.00822117
[145] valid_0's l1: 0.0632689 valid_0's l2: 0.00819275
[146] valid_0's l1: 0.0632355 valid_0's l2: 0.0081842
[147] valid_0's l1: 0.0631734 valid_0's l2: 0.00816914
[148] valid_0's l1: 0.0631554 valid_0's l2: 0.00816342
[149] valid_0's l1: 0.0630915 valid_0's l2: 0.00814223
[150] valid_0's l1: 0.0630257 valid_0's l2: 0.00812864
[151] valid_0's l1: 0.0629674 valid_0's l2: 0.00810772
[152] valid_0's l1: 0.0628928 valid_0's l2: 0.00809157
[153] valid_0's l1: 0.062862 valid_0's l2: 0.00808326
[154] valid_0's l1: 0.0628367 valid_0's l2: 0.00807544
[155] valid_0's l1: 0.0628224 valid_0's l2: 0.00807054
[156] valid_0's l1: 0.0627607 valid_0's l2: 0.00805709
[157] valid_0's l1: 0.0627235 valid_0's l2: 0.00804171
[158] valid_0's l1: 0.0626681 valid_0's l2: 0.00802948
[159] valid_0's l1: 0.0626548 valid_0's l2: 0.00802659
[160] valid_0's l1: 0.0625744 valid_0's l2: 0.00800215
[161] valid_0's l1: 0.0625507 valid_0's l2: 0.00799773
[162] valid_0's l1: 0.0625062 valid_0's l2: 0.00798584
[163] valid_0's l1: 0.0624752 valid_0's l2: 0.00797277
[164] valid_0's l1: 0.0624702 valid_0's l2: 0.0079688
[165] valid_0's l1: 0.0624439 valid_0's l2: 0.00796345
[166] valid_0's l1: 0.0624375 valid_0's l2: 0.00796167
[167] valid_0's l1: 0.0624021 valid_0's l2: 0.00795622
[168] valid_0's l1: 0.0623861 valid_0's l2: 0.0079532
[169] valid_0's l1: 0.0623754 valid_0's l2: 0.00795102
[170] valid_0's l1: 0.0623463 valid_0's l2: 0.00794603
[171] valid_0's l1: 0.0622781 valid_0's l2: 0.00793724
[172] valid_0's l1: 0.0622417 valid_0's l2: 0.00792782
[173] valid_0's l1: 0.0622139 valid_0's l2: 0.00791893
[174] valid_0's l1: 0.0621415 valid_0's l2: 0.00790142
[175] valid_0's l1: 0.0621292 valid_0's l2: 0.00790014
[176] valid_0's l1: 0.0621091 valid_0's l2: 0.00789475
[177] valid_0's l1: 0.0620937 valid_0's l2: 0.00789072
[178] valid_0's l1: 0.0620923 valid_0's l2: 0.00788742
[179] valid_0's l1: 0.0620695 valid_0's l2: 0.00788345
[180] valid_0's l1: 0.0620561 valid_0's l2: 0.00787245
[181] valid_0's l1: 0.0619938 valid_0's l2: 0.00785701
[182] valid_0's l1: 0.0619728 valid_0's l2: 0.00785054
[183] valid_0's l1: 0.0619483 valid_0's l2: 0.00784426
[184] valid_0's l1: 0.0618916 valid_0's l2: 0.00782893
[185] valid_0's l1: 0.0618882 valid_0's l2: 0.00782821
[186] valid_0's l1: 0.0617958 valid_0's l2: 0.00780013
[187] valid_0's l1: 0.0617874 valid_0's l2: 0.00779428
[188] valid_0's l1: 0.0617613 valid_0's l2: 0.00778537
[189] valid_0's l1: 0.0617498 valid_0's l2: 0.00778327
[190] valid_0's l1: 0.0617415 valid_0's l2: 0.00778013
[191] valid_0's l1: 0.0617271 valid_0's l2: 0.00777387
[192] valid_0's l1: 0.0617194 valid_0's l2: 0.00777251
[193] valid_0's l1: 0.0617034 valid_0's l2: 0.00776778
[194] valid_0's l1: 0.0616932 valid_0's l2: 0.00776213
[195] valid_0's l1: 0.0616458 valid_0's l2: 0.00775323
[196] valid_0's l1: 0.0616342 valid_0's l2: 0.0077498
[197] valid_0's l1: 0.0616099 valid_0's l2: 0.00774467
[198] valid_0's l1: 0.0615941 valid_0's l2: 0.00774147
[199] valid_0's l1: 0.0615867 valid_0's l2: 0.00773916
[200] valid_0's l1: 0.0615496 valid_0's l2: 0.00773484
[201] valid_0's l1: 0.0615442 valid_0's l2: 0.00773016
[202] valid_0's l1: 0.0615308 valid_0's l2: 0.00772869
[203] valid_0's l1: 0.061527 valid_0's l2: 0.00772763
[204] valid_0's l1: 0.0615127 valid_0's l2: 0.00772413
[205] valid_0's l1: 0.0614676 valid_0's l2: 0.00770868
[206] valid_0's l1: 0.0614456 valid_0's l2: 0.00770356
[207] valid_0's l1: 0.0614349 valid_0's l2: 0.00770067
[208] valid_0's l1: 0.0614148 valid_0's l2: 0.00769728
[209] valid_0's l1: 0.061393 valid_0's l2: 0.00769306
[210] valid_0's l1: 0.0613878 valid_0's l2: 0.00769176
[211] valid_0's l1: 0.0613715 valid_0's l2: 0.00768739
[212] valid_0's l1: 0.0613692 valid_0's l2: 0.00768421
[213] valid_0's l1: 0.0613009 valid_0's l2: 0.00767403
[214] valid_0's l1: 0.061259 valid_0's l2: 0.00766591
[215] valid_0's l1: 0.0612563 valid_0's l2: 0.00766338
[216] valid_0's l1: 0.0612416 valid_0's l2: 0.00766072
[217] valid_0's l1: 0.0612324 valid_0's l2: 0.00765844
[218] valid_0's l1: 0.0612154 valid_0's l2: 0.00765249
[219] valid_0's l1: 0.0611909 valid_0's l2: 0.00764667
[220] valid_0's l1: 0.0611841 valid_0's l2: 0.00764471
[221] valid_0's l1: 0.0611569 valid_0's l2: 0.00763774
[222] valid_0's l1: 0.0611428 valid_0's l2: 0.00763603
[223] valid_0's l1: 0.0611038 valid_0's l2: 0.00762949
[224] valid_0's l1: 0.0610883 valid_0's l2: 0.00762472
[225] valid_0's l1: 0.0610858 valid_0's l2: 0.00762472
[226] valid_0's l1: 0.0610785 valid_0's l2: 0.00762216
[227] valid_0's l1: 0.0610739 valid_0's l2: 0.00762143
[228] valid_0's l1: 0.0610678 valid_0's l2: 0.00761936
[229] valid_0's l1: 0.0610439 valid_0's l2: 0.00761486
[230] valid_0's l1: 0.0610175 valid_0's l2: 0.00760908
[231] valid_0's l1: 0.0609923 valid_0's l2: 0.00760436
[232] valid_0's l1: 0.0609525 valid_0's l2: 0.00759502
[233] valid_0's l1: 0.0609434 valid_0's l2: 0.00759245
[234] valid_0's l1: 0.0609346 valid_0's l2: 0.00758803
[235] valid_0's l1: 0.0609314 valid_0's l2: 0.00758588
[236] valid_0's l1: 0.0609282 valid_0's l2: 0.0075855
[237] valid_0's l1: 0.0609238 valid_0's l2: 0.00758473
[238] valid_0's l1: 0.060858 valid_0's l2: 0.0075607
[239] valid_0's l1: 0.0608407 valid_0's l2: 0.00755626
[240] valid_0's l1: 0.0608279 valid_0's l2: 0.00755207
[241] valid_0's l1: 0.060826 valid_0's l2: 0.0075503
[242] valid_0's l1: 0.0607948 valid_0's l2: 0.00754333
[243] valid_0's l1: 0.0607598 valid_0's l2: 0.00753284
[244] valid_0's l1: 0.0607413 valid_0's l2: 0.00753085
[245] valid_0's l1: 0.0607301 valid_0's l2: 0.00752813
[246] valid_0's l1: 0.0607071 valid_0's l2: 0.00752255
[247] valid_0's l1: 0.0607026 valid_0's l2: 0.00752137
[248] valid_0's l1: 0.0606923 valid_0's l2: 0.00751851
[249] valid_0's l1: 0.0606846 valid_0's l2: 0.00751692
[250] valid_0's l1: 0.0606751 valid_0's l2: 0.00751755
[251] valid_0's l1: 0.0606774 valid_0's l2: 0.00751856
[252] valid_0's l1: 0.0606561 valid_0's l2: 0.00751333
[253] valid_0's l1: 0.0606549 valid_0's l2: 0.00751255
[254] valid_0's l1: 0.0606527 valid_0's l2: 0.0075117
[255] valid_0's l1: 0.0606451 valid_0's l2: 0.00750979
[256] valid_0's l1: 0.0606465 valid_0's l2: 0.00750681
[257] valid_0's l1: 0.0606421 valid_0's l2: 0.00750484
[258] valid_0's l1: 0.0605991 valid_0's l2: 0.00749648
[259] valid_0's l1: 0.0605624 valid_0's l2: 0.00748836
[260] valid_0's l1: 0.0605564 valid_0's l2: 0.00748607
[261] valid_0's l1: 0.060547 valid_0's l2: 0.00747954
[262] valid_0's l1: 0.060511 valid_0's l2: 0.00747297
[263] valid_0's l1: 0.0604954 valid_0's l2: 0.00746919
[264] valid_0's l1: 0.0604873 valid_0's l2: 0.00746591
[265] valid_0's l1: 0.0604839 valid_0's l2: 0.00746563
[266] valid_0's l1: 0.0604628 valid_0's l2: 0.00745981
[267] valid_0's l1: 0.0604221 valid_0's l2: 0.0074482
[268] valid_0's l1: 0.0604221 valid_0's l2: 0.00744695
[269] valid_0's l1: 0.0604059 valid_0's l2: 0.00744225
[270] valid_0's l1: 0.060387 valid_0's l2: 0.00743872
[271] valid_0's l1: 0.0603783 valid_0's l2: 0.00743707
[272] valid_0's l1: 0.0603586 valid_0's l2: 0.00743586
[273] valid_0's l1: 0.0603492 valid_0's l2: 0.00743342
[274] valid_0's l1: 0.0603445 valid_0's l2: 0.00743217
[275] valid_0's l1: 0.0603204 valid_0's l2: 0.00742736
[276] valid_0's l1: 0.0602933 valid_0's l2: 0.0074182
[277] valid_0's l1: 0.0602856 valid_0's l2: 0.00741636
[278] valid_0's l1: 0.0602808 valid_0's l2: 0.00741444
[279] valid_0's l1: 0.0602755 valid_0's l2: 0.00741346
[280] valid_0's l1: 0.060249 valid_0's l2: 0.0074111
[281] valid_0's l1: 0.060246 valid_0's l2: 0.00740991
[282] valid_0's l1: 0.0602039 valid_0's l2: 0.00740377
[283] valid_0's l1: 0.0601958 valid_0's l2: 0.00739957
[284] valid_0's l1: 0.0601844 valid_0's l2: 0.00739784
[285] valid_0's l1: 0.0601649 valid_0's l2: 0.00738912
[286] valid_0's l1: 0.0601514 valid_0's l2: 0.00738531
[287] valid_0's l1: 0.060144 valid_0's l2: 0.0073847
[288] valid_0's l1: 0.0601185 valid_0's l2: 0.00737609
[289] valid_0's l1: 0.060093 valid_0's l2: 0.00736866
[290] valid_0's l1: 0.0600861 valid_0's l2: 0.00736682
[291] valid_0's l1: 0.060082 valid_0's l2: 0.00736437
[292] valid_0's l1: 0.0600697 valid_0's l2: 0.00736088
[293] valid_0's l1: 0.0600644 valid_0's l2: 0.00736019
[294] valid_0's l1: 0.0600478 valid_0's l2: 0.00735573
[295] valid_0's l1: 0.0600324 valid_0's l2: 0.00735283
[296] valid_0's l1: 0.0600261 valid_0's l2: 0.00735116
[297] valid_0's l1: 0.060015 valid_0's l2: 0.00734777
[298] valid_0's l1: 0.0599943 valid_0's l2: 0.00734292
[299] valid_0's l1: 0.0599897 valid_0's l2: 0.0073379
[300] valid_0's l1: 0.0599743 valid_0's l2: 0.00733289
[301] valid_0's l1: 0.0599677 valid_0's l2: 0.00733165
[302] valid_0's l1: 0.0599564 valid_0's l2: 0.00732815
[303] valid_0's l1: 0.0599186 valid_0's l2: 0.00731642
[304] valid_0's l1: 0.0599057 valid_0's l2: 0.00731443
[305] valid_0's l1: 0.0598899 valid_0's l2: 0.00731051
[306] valid_0's l1: 0.0598758 valid_0's l2: 0.00730785
[307] valid_0's l1: 0.059867 valid_0's l2: 0.00730554
[308] valid_0's l1: 0.0598299 valid_0's l2: 0.00729924
[309] valid_0's l1: 0.0598213 valid_0's l2: 0.00729622
[310] valid_0's l1: 0.0598019 valid_0's l2: 0.00729078
[311] valid_0's l1: 0.0597985 valid_0's l2: 0.00729032
[312] valid_0's l1: 0.0597968 valid_0's l2: 0.00728993
[313] valid_0's l1: 0.0597889 valid_0's l2: 0.00728781
[314] valid_0's l1: 0.0597828 valid_0's l2: 0.00728647
[315] valid_0's l1: 0.0597752 valid_0's l2: 0.00728308
[316] valid_0's l1: 0.0597313 valid_0's l2: 0.00727217
[317] valid_0's l1: 0.0597099 valid_0's l2: 0.00727033
[318] valid_0's l1: 0.0597052 valid_0's l2: 0.00726923
[319] valid_0's l1: 0.0596815 valid_0's l2: 0.00726406
[320] valid_0's l1: 0.0596641 valid_0's l2: 0.00725938
[321] valid_0's l1: 0.0596434 valid_0's l2: 0.00725662
[322] valid_0's l1: 0.0596271 valid_0's l2: 0.00725326
[323] valid_0's l1: 0.0596116 valid_0's l2: 0.00724937
[324] valid_0's l1: 0.0596097 valid_0's l2: 0.00724817
[325] valid_0's l1: 0.0596057 valid_0's l2: 0.00724688
[326] valid_0's l1: 0.0596048 valid_0's l2: 0.00724418
[327] valid_0's l1: 0.0596012 valid_0's l2: 0.00724299
[328] valid_0's l1: 0.059593 valid_0's l2: 0.00724168
[329] valid_0's l1: 0.0595802 valid_0's l2: 0.00723676
[330] valid_0's l1: 0.0595807 valid_0's l2: 0.00723609
[331] valid_0's l1: 0.0595654 valid_0's l2: 0.00723407
[332] valid_0's l1: 0.059557 valid_0's l2: 0.00723122
[333] valid_0's l1: 0.0595544 valid_0's l2: 0.00723015
[334] valid_0's l1: 0.0595488 valid_0's l2: 0.00722858
[335] valid_0's l1: 0.0595438 valid_0's l2: 0.00722837
[336] valid_0's l1: 0.0595339 valid_0's l2: 0.00722712
[337] valid_0's l1: 0.0595153 valid_0's l2: 0.00722262
[338] valid_0's l1: 0.0595139 valid_0's l2: 0.00722096
[339] valid_0's l1: 0.0594884 valid_0's l2: 0.0072162
[340] valid_0's l1: 0.059482 valid_0's l2: 0.00721469
[341] valid_0's l1: 0.0594803 valid_0's l2: 0.00721375
[342] valid_0's l1: 0.0594763 valid_0's l2: 0.0072131
[343] valid_0's l1: 0.0594707 valid_0's l2: 0.00721182
[344] valid_0's l1: 0.0594612 valid_0's l2: 0.00721005
[345] valid_0's l1: 0.0594647 valid_0's l2: 0.00721021
[346] valid_0's l1: 0.0594596 valid_0's l2: 0.00721017
[347] valid_0's l1: 0.0594374 valid_0's l2: 0.00720848
[348] valid_0's l1: 0.0594418 valid_0's l2: 0.00720804
[349] valid_0's l1: 0.0594385 valid_0's l2: 0.00720749
[350] valid_0's l1: 0.0594349 valid_0's l2: 0.00720642
[351] valid_0's l1: 0.0594172 valid_0's l2: 0.00720432
[352] valid_0's l1: 0.0594158 valid_0's l2: 0.00720268
[353] valid_0's l1: 0.0594119 valid_0's l2: 0.00720244
[354] valid_0's l1: 0.0594084 valid_0's l2: 0.00720234
[355] valid_0's l1: 0.0594068 valid_0's l2: 0.00720142
[356] valid_0's l1: 0.0593827 valid_0's l2: 0.00719698
[357] valid_0's l1: 0.0593672 valid_0's l2: 0.00719361
[358] valid_0's l1: 0.0593657 valid_0's l2: 0.0071925
[359] valid_0's l1: 0.0593621 valid_0's l2: 0.00719183
[360] valid_0's l1: 0.0593578 valid_0's l2: 0.00719048
[361] valid_0's l1: 0.0593317 valid_0's l2: 0.00718509
[362] valid_0's l1: 0.0593165 valid_0's l2: 0.00718096
[363] valid_0's l1: 0.0592964 valid_0's l2: 0.00717612
[364] valid_0's l1: 0.0592904 valid_0's l2: 0.00717273
[365] valid_0's l1: 0.0592745 valid_0's l2: 0.00716989
[366] valid_0's l1: 0.0592595 valid_0's l2: 0.00716674
[367] valid_0's l1: 0.0592458 valid_0's l2: 0.00716225
[368] valid_0's l1: 0.059247 valid_0's l2: 0.00716195
[369] valid_0's l1: 0.0592436 valid_0's l2: 0.00716071
[370] valid_0's l1: 0.0592314 valid_0's l2: 0.00715802
[371] valid_0's l1: 0.0592317 valid_0's l2: 0.00715905
[372] valid_0's l1: 0.0592257 valid_0's l2: 0.00715697
[373] valid_0's l1: 0.0592199 valid_0's l2: 0.00715637
[374] valid_0's l1: 0.0592168 valid_0's l2: 0.00715498
[375] valid_0's l1: 0.0592069 valid_0's l2: 0.0071537
[376] valid_0's l1: 0.0591999 valid_0's l2: 0.00715176
[377] valid_0's l1: 0.0591984 valid_0's l2: 0.00714892
[378] valid_0's l1: 0.0591692 valid_0's l2: 0.00713848
[379] valid_0's l1: 0.0591685 valid_0's l2: 0.00713698
[380] valid_0's l1: 0.0591578 valid_0's l2: 0.00713306
[381] valid_0's l1: 0.0591421 valid_0's l2: 0.00713099
[382] valid_0's l1: 0.0591172 valid_0's l2: 0.00712579
[383] valid_0's l1: 0.0591006 valid_0's l2: 0.0071224
[384] valid_0's l1: 0.059097 valid_0's l2: 0.00712188
[385] valid_0's l1: 0.059095 valid_0's l2: 0.00712107
[386] valid_0's l1: 0.0590862 valid_0's l2: 0.00711945
[387] valid_0's l1: 0.0590644 valid_0's l2: 0.00711505
[388] valid_0's l1: 0.0590607 valid_0's l2: 0.00711506
[389] valid_0's l1: 0.0590575 valid_0's l2: 0.00711392
[390] valid_0's l1: 0.0590417 valid_0's l2: 0.00711146
[391] valid_0's l1: 0.0590355 valid_0's l2: 0.00711067
[392] valid_0's l1: 0.0590286 valid_0's l2: 0.00710778
[393] valid_0's l1: 0.0590216 valid_0's l2: 0.00710659
[394] valid_0's l1: 0.0590099 valid_0's l2: 0.00710301
[395] valid_0's l1: 0.0590069 valid_0's l2: 0.00710208
[396] valid_0's l1: 0.0590034 valid_0's l2: 0.00710047
[397] valid_0's l1: 0.0589909 valid_0's l2: 0.0070936
[398] valid_0's l1: 0.0589901 valid_0's l2: 0.0070914
[399] valid_0's l1: 0.0589854 valid_0's l2: 0.00708941
[400] valid_0's l1: 0.0589705 valid_0's l2: 0.00708773
[401] valid_0's l1: 0.0589625 valid_0's l2: 0.00708597
[402] valid_0's l1: 0.0589644 valid_0's l2: 0.00708488
[403] valid_0's l1: 0.0589634 valid_0's l2: 0.00708438
[404] valid_0's l1: 0.0589565 valid_0's l2: 0.00708323
[405] valid_0's l1: 0.0589561 valid_0's l2: 0.00708292
[406] valid_0's l1: 0.0589485 valid_0's l2: 0.00708057
[407] valid_0's l1: 0.0589426 valid_0's l2: 0.00707874
[408] valid_0's l1: 0.0589328 valid_0's l2: 0.0070761
[409] valid_0's l1: 0.058926 valid_0's l2: 0.007073
[410] valid_0's l1: 0.0589203 valid_0's l2: 0.00707211
[411] valid_0's l1: 0.0588941 valid_0's l2: 0.00706425
[412] valid_0's l1: 0.058887 valid_0's l2: 0.00706222
[413] valid_0's l1: 0.0588788 valid_0's l2: 0.00705912
[414] valid_0's l1: 0.0588677 valid_0's l2: 0.00705691
[415] valid_0's l1: 0.0588715 valid_0's l2: 0.00706103
[416] valid_0's l1: 0.0588646 valid_0's l2: 0.00705964
[417] valid_0's l1: 0.0588604 valid_0's l2: 0.00705951
[418] valid_0's l1: 0.0588378 valid_0's l2: 0.00705833
[419] valid_0's l1: 0.0588364 valid_0's l2: 0.00705713
Early stopping, best iteration is:
[414] valid_0's l1: 0.0588677 valid_0's l2: 0.00705691
本次結果輸出的mae值是:
0.058867698663447106
[1] valid_0's l1: 0.244506 valid_0's l2: 0.0790859
Training until validation scores don't improve for 5 rounds
[2] valid_0's l1: 0.223857 valid_0's l2: 0.0667341
[3] valid_0's l1: 0.205626 valid_0's l2: 0.0566941
[4] valid_0's l1: 0.189298 valid_0's l2: 0.0484539
[5] valid_0's l1: 0.175104 valid_0's l2: 0.0417921
[6] valid_0's l1: 0.16227 valid_0's l2: 0.036285
[7] valid_0's l1: 0.150898 valid_0's l2: 0.0317221
[8] valid_0's l1: 0.140883 valid_0's l2: 0.0280196
[9] valid_0's l1: 0.131939 valid_0's l2: 0.0249613
[10] valid_0's l1: 0.124375 valid_0's l2: 0.0224944
[11] valid_0's l1: 0.117467 valid_0's l2: 0.0204263
[12] valid_0's l1: 0.110957 valid_0's l2: 0.0185453
[13] valid_0's l1: 0.105077 valid_0's l2: 0.016929
[14] valid_0's l1: 0.100272 valid_0's l2: 0.0157152
[15] valid_0's l1: 0.0960833 valid_0's l2: 0.0147431
[16] valid_0's l1: 0.0920567 valid_0's l2: 0.0137979
[17] valid_0's l1: 0.088268 valid_0's l2: 0.0129121
[18] valid_0's l1: 0.0854499 valid_0's l2: 0.0123437
[19] valid_0's l1: 0.0827065 valid_0's l2: 0.0117755
[20] valid_0's l1: 0.0800967 valid_0's l2: 0.0112162
[21] valid_0's l1: 0.0781237 valid_0's l2: 0.0108399
[22] valid_0's l1: 0.0763776 valid_0's l2: 0.0105204
[23] valid_0's l1: 0.074906 valid_0's l2: 0.010265
[24] valid_0's l1: 0.0732985 valid_0's l2: 0.00994854
[25] valid_0's l1: 0.0720481 valid_0's l2: 0.00970346
[26] valid_0's l1: 0.0710837 valid_0's l2: 0.00953733
[27] valid_0's l1: 0.0703066 valid_0's l2: 0.00939487
[28] valid_0's l1: 0.0694075 valid_0's l2: 0.009231
[29] valid_0's l1: 0.0685866 valid_0's l2: 0.00907982
[30] valid_0's l1: 0.06779 valid_0's l2: 0.0089391
[31] valid_0's l1: 0.067074 valid_0's l2: 0.00880224
[32] valid_0's l1: 0.0665149 valid_0's l2: 0.00871073
[33] valid_0's l1: 0.0659205 valid_0's l2: 0.00858992
[34] valid_0's l1: 0.0652426 valid_0's l2: 0.00843778
[35] valid_0's l1: 0.0648768 valid_0's l2: 0.00836372
[36] valid_0's l1: 0.0645306 valid_0's l2: 0.00830614
[37] valid_0's l1: 0.0640175 valid_0's l2: 0.00818645
[38] valid_0's l1: 0.0637947 valid_0's l2: 0.00814636
[39] valid_0's l1: 0.0635605 valid_0's l2: 0.00810447
[40] valid_0's l1: 0.0633449 valid_0's l2: 0.00806796
[41] valid_0's l1: 0.0631446 valid_0's l2: 0.00802436
[42] valid_0's l1: 0.0627575 valid_0's l2: 0.00792715
[43] valid_0's l1: 0.062611 valid_0's l2: 0.00789809
[44] valid_0's l1: 0.0623997 valid_0's l2: 0.00785422
[45] valid_0's l1: 0.0622728 valid_0's l2: 0.00783369
[46] valid_0's l1: 0.0621336 valid_0's l2: 0.00781042
[47] valid_0's l1: 0.0619906 valid_0's l2: 0.00777463
[48] valid_0's l1: 0.0618556 valid_0's l2: 0.00774471
[49] valid_0's l1: 0.0617449 valid_0's l2: 0.00772713
[50] valid_0's l1: 0.0616454 valid_0's l2: 0.00770345
[51] valid_0's l1: 0.0615492 valid_0's l2: 0.00768433
[52] valid_0's l1: 0.0614452 valid_0's l2: 0.00766572
[53] valid_0's l1: 0.0613725 valid_0's l2: 0.00764408
[54] valid_0's l1: 0.0612796 valid_0's l2: 0.00762834
[55] valid_0's l1: 0.0611706 valid_0's l2: 0.00760066
[56] valid_0's l1: 0.0611121 valid_0's l2: 0.00759012
[57] valid_0's l1: 0.0610472 valid_0's l2: 0.00757864
[58] valid_0's l1: 0.0609801 valid_0's l2: 0.00756664
[59] valid_0's l1: 0.0608993 valid_0's l2: 0.00755207
[60] valid_0's l1: 0.0608206 valid_0's l2: 0.00753343
[61] valid_0's l1: 0.0607751 valid_0's l2: 0.00752435
[62] valid_0's l1: 0.0607163 valid_0's l2: 0.00751128
[63] valid_0's l1: 0.0605746 valid_0's l2: 0.00747752
[64] valid_0's l1: 0.0604651 valid_0's l2: 0.0074507
[65] valid_0's l1: 0.0604263 valid_0's l2: 0.00744162
[66] valid_0's l1: 0.0603599 valid_0's l2: 0.00742889
[67] valid_0's l1: 0.0602701 valid_0's l2: 0.00741169
[68] valid_0's l1: 0.0602334 valid_0's l2: 0.00739977
[69] valid_0's l1: 0.060112 valid_0's l2: 0.007374
[70] valid_0's l1: 0.0600257 valid_0's l2: 0.00735234
[71] valid_0's l1: 0.059928 valid_0's l2: 0.00732775
[72] valid_0's l1: 0.0598568 valid_0's l2: 0.00731406
[73] valid_0's l1: 0.0598019 valid_0's l2: 0.007307
[74] valid_0's l1: 0.0597175 valid_0's l2: 0.00728248
[75] valid_0's l1: 0.0596246 valid_0's l2: 0.00726385
[76] valid_0's l1: 0.0596029 valid_0's l2: 0.00725546
[77] valid_0's l1: 0.0595231 valid_0's l2: 0.00723235
[78] valid_0's l1: 0.0595014 valid_0's l2: 0.00722831
[79] valid_0's l1: 0.0594712 valid_0's l2: 0.00722155
[80] valid_0's l1: 0.0594512 valid_0's l2: 0.00721585
[81] valid_0's l1: 0.0594197 valid_0's l2: 0.00720856
[82] valid_0's l1: 0.0593932 valid_0's l2: 0.00720475
[83] valid_0's l1: 0.0593157 valid_0's l2: 0.00718559
[84] valid_0's l1: 0.0592926 valid_0's l2: 0.00718174
[85] valid_0's l1: 0.0592627 valid_0's l2: 0.00717558
[86] valid_0's l1: 0.0592466 valid_0's l2: 0.00716984
[87] valid_0's l1: 0.0592043 valid_0's l2: 0.00716193
[88] valid_0's l1: 0.0591298 valid_0's l2: 0.00714342
[89] valid_0's l1: 0.059097 valid_0's l2: 0.00713607
[90] valid_0's l1: 0.0589982 valid_0's l2: 0.00711745
[91] valid_0's l1: 0.0589505 valid_0's l2: 0.00710413
[92] valid_0's l1: 0.0588884 valid_0's l2: 0.00708708
[93] valid_0's l1: 0.0588084 valid_0's l2: 0.00707062
[94] valid_0's l1: 0.0587967 valid_0's l2: 0.00707269
[95] valid_0's l1: 0.0587689 valid_0's l2: 0.00706628
[96] valid_0's l1: 0.058749 valid_0's l2: 0.00705746
[97] valid_0's l1: 0.0587121 valid_0's l2: 0.00704977
[98] valid_0's l1: 0.0586712 valid_0's l2: 0.00703874
[99] valid_0's l1: 0.05861 valid_0's l2: 0.0070237
[100] valid_0's l1: 0.0585889 valid_0's l2: 0.00701449
[101] valid_0's l1: 0.0585771 valid_0's l2: 0.00701003
[102] valid_0's l1: 0.0585635 valid_0's l2: 0.0070058
[103] valid_0's l1: 0.0585479 valid_0's l2: 0.00700123
[104] valid_0's l1: 0.0585384 valid_0's l2: 0.00699934
[105] valid_0's l1: 0.0585209 valid_0's l2: 0.00699689
[106] valid_0's l1: 0.0585063 valid_0's l2: 0.00699365
[107] valid_0's l1: 0.0584905 valid_0's l2: 0.00698833
[108] valid_0's l1: 0.0584738 valid_0's l2: 0.00698361
[109] valid_0's l1: 0.0584563 valid_0's l2: 0.00697852
[110] valid_0's l1: 0.0584379 valid_0's l2: 0.00697721
[111] valid_0's l1: 0.0584333 valid_0's l2: 0.00697385
[112] valid_0's l1: 0.0584316 valid_0's l2: 0.00697371
[113] valid_0's l1: 0.0584076 valid_0's l2: 0.00696515
[114] valid_0's l1: 0.0583903 valid_0's l2: 0.00696145
[115] valid_0's l1: 0.0583817 valid_0's l2: 0.00695705
[116] valid_0's l1: 0.0583585 valid_0's l2: 0.00695171
[117] valid_0's l1: 0.0583329 valid_0's l2: 0.00694439
[118] valid_0's l1: 0.0582953 valid_0's l2: 0.00693451
[119] valid_0's l1: 0.0582302 valid_0's l2: 0.00691471
[120] valid_0's l1: 0.0582053 valid_0's l2: 0.00690717
[121] valid_0's l1: 0.058165 valid_0's l2: 0.00690191
[122] valid_0's l1: 0.0581507 valid_0's l2: 0.00689919
[123] valid_0's l1: 0.0581202 valid_0's l2: 0.00689344
[124] valid_0's l1: 0.0580842 valid_0's l2: 0.00688742
[125] valid_0's l1: 0.0580695 valid_0's l2: 0.00688381
[126] valid_0's l1: 0.0580556 valid_0's l2: 0.0068812
[127] valid_0's l1: 0.0580404 valid_0's l2: 0.00687439
[128] valid_0's l1: 0.0580276 valid_0's l2: 0.00687128
[129] valid_0's l1: 0.0579982 valid_0's l2: 0.00686654
[130] valid_0's l1: 0.0579886 valid_0's l2: 0.00686163
[131] valid_0's l1: 0.0579795 valid_0's l2: 0.00686265
[132] valid_0's l1: 0.0579773 valid_0's l2: 0.00686113
[133] valid_0's l1: 0.0579718 valid_0's l2: 0.00686023
[134] valid_0's l1: 0.0579558 valid_0's l2: 0.00685794
[135] valid_0's l1: 0.0579452 valid_0's l2: 0.00685478
[136] valid_0's l1: 0.0579375 valid_0's l2: 0.00685219
[137] valid_0's l1: 0.0579311 valid_0's l2: 0.00685004
[138] valid_0's l1: 0.0578929 valid_0's l2: 0.0068395
[139] valid_0's l1: 0.0578863 valid_0's l2: 0.00683737
[140] valid_0's l1: 0.0578618 valid_0's l2: 0.00682973
[141] valid_0's l1: 0.057814 valid_0's l2: 0.00682006
[142] valid_0's l1: 0.0577865 valid_0's l2: 0.00681178
[143] valid_0's l1: 0.0577836 valid_0's l2: 0.00681092
[144] valid_0's l1: 0.0577732 valid_0's l2: 0.00680807
[145] valid_0's l1: 0.05775 valid_0's l2: 0.00680476
[146] valid_0's l1: 0.0577295 valid_0's l2: 0.00679983
[147] valid_0's l1: 0.0577274 valid_0's l2: 0.00679906
[148] valid_0's l1: 0.0577282 valid_0's l2: 0.00680071
[149] valid_0's l1: 0.0577027 valid_0's l2: 0.00679154
[150] valid_0's l1: 0.0577023 valid_0's l2: 0.00679031
[151] valid_0's l1: 0.0576895 valid_0's l2: 0.0067869
[152] valid_0's l1: 0.0576863 valid_0's l2: 0.00678669
[153] valid_0's l1: 0.057681 valid_0's l2: 0.00678298
[154] valid_0's l1: 0.0576461 valid_0's l2: 0.00677587
[155] valid_0's l1: 0.0576281 valid_0's l2: 0.00676922
[156] valid_0's l1: 0.0576071 valid_0's l2: 0.00676582
[157] valid_0's l1: 0.0575831 valid_0's l2: 0.00676433
[158] valid_0's l1: 0.0575486 valid_0's l2: 0.00675594
[159] valid_0's l1: 0.0575371 valid_0's l2: 0.00675428
[160] valid_0's l1: 0.0575191 valid_0's l2: 0.00675119
[161] valid_0's l1: 0.0575124 valid_0's l2: 0.00675132
[162] valid_0's l1: 0.0575015 valid_0's l2: 0.00674851
[163] valid_0's l1: 0.0574785 valid_0's l2: 0.00674612
[164] valid_0's l1: 0.0574678 valid_0's l2: 0.00674357
[165] valid_0's l1: 0.0574633 valid_0's l2: 0.00674366
[166] valid_0's l1: 0.0574458 valid_0's l2: 0.00673905
[167] valid_0's l1: 0.0574112 valid_0's l2: 0.00672946
[168] valid_0's l1: 0.0573985 valid_0's l2: 0.00672684
[169] valid_0's l1: 0.0573967 valid_0's l2: 0.00672339
[170] valid_0's l1: 0.0573916 valid_0's l2: 0.00672143
[171] valid_0's l1: 0.0573821 valid_0's l2: 0.00671896
[172] valid_0's l1: 0.0573703 valid_0's l2: 0.00671675
[173] valid_0's l1: 0.0573778 valid_0's l2: 0.00671835
[174] valid_0's l1: 0.0573707 valid_0's l2: 0.00671666
[175] valid_0's l1: 0.0573572 valid_0's l2: 0.00671479
[176] valid_0's l1: 0.0573574 valid_0's l2: 0.0067135
[177] valid_0's l1: 0.0573542 valid_0's l2: 0.00671297
[178] valid_0's l1: 0.057355 valid_0's l2: 0.00671458
[179] valid_0's l1: 0.0573403 valid_0's l2: 0.00671148
[180] valid_0's l1: 0.0573278 valid_0's l2: 0.00670699
[181] valid_0's l1: 0.0573277 valid_0's l2: 0.00670656
[182] valid_0's l1: 0.0573192 valid_0's l2: 0.00670432
[183] valid_0's l1: 0.0573012 valid_0's l2: 0.00670044
[184] valid_0's l1: 0.0573018 valid_0's l2: 0.00669917
[185] valid_0's l1: 0.0572933 valid_0's l2: 0.00669791
[186] valid_0's l1: 0.0572937 valid_0's l2: 0.00669628
[187] valid_0's l1: 0.0572959 valid_0's l2: 0.006696
[188] valid_0's l1: 0.0572946 valid_0's l2: 0.00669529
[189] valid_0's l1: 0.057284 valid_0's l2: 0.00669419
[190] valid_0's l1: 0.0572756 valid_0's l2: 0.00669183
[191] valid_0's l1: 0.0572536 valid_0's l2: 0.00668716
[192] valid_0's l1: 0.0572549 valid_0's l2: 0.0066889
[193] valid_0's l1: 0.0572407 valid_0's l2: 0.00668565
[194] valid_0's l1: 0.0572137 valid_0's l2: 0.00668092
[195] valid_0's l1: 0.0572176 valid_0's l2: 0.00668088
[196] valid_0's l1: 0.0571987 valid_0's l2: 0.00667881
[197] valid_0's l1: 0.0571904 valid_0's l2: 0.00667394
[198] valid_0's l1: 0.0571825 valid_0's l2: 0.00667086
[199] valid_0's l1: 0.0571814 valid_0's l2: 0.0066715
[200] valid_0's l1: 0.0571754 valid_0's l2: 0.0066712
[201] valid_0's l1: 0.0571679 valid_0's l2: 0.00666813
[202] valid_0's l1: 0.0571538 valid_0's l2: 0.00666858
[203] valid_0's l1: 0.0571505 valid_0's l2: 0.00666737
[204] valid_0's l1: 0.0571496 valid_0's l2: 0.00666642
[205] valid_0's l1: 0.0571438 valid_0's l2: 0.00666369
[206] valid_0's l1: 0.0571522 valid_0's l2: 0.0066647
[207] valid_0's l1: 0.0571443 valid_0's l2: 0.00666353
[208] valid_0's l1: 0.057147 valid_0's l2: 0.00666341
[209] valid_0's l1: 0.0571413 valid_0's l2: 0.00666194
[210] valid_0's l1: 0.0571264 valid_0's l2: 0.00666014
[211] valid_0's l1: 0.0571122 valid_0's l2: 0.00665585
[212] valid_0's l1: 0.0571118 valid_0's l2: 0.00665518
[213] valid_0's l1: 0.0571002 valid_0's l2: 0.00665338
[214] valid_0's l1: 0.0570964 valid_0's l2: 0.00665241
[215] valid_0's l1: 0.05709 valid_0's l2: 0.00665055
[216] valid_0's l1: 0.0570856 valid_0's l2: 0.00665057
[217] valid_0's l1: 0.0570826 valid_0's l2: 0.00665004
[218] valid_0's l1: 0.0570747 valid_0's l2: 0.00664898
[219] valid_0's l1: 0.057068 valid_0's l2: 0.00664791
[220] valid_0's l1: 0.05707 valid_0's l2: 0.00664691
[221] valid_0's l1: 0.0570675 valid_0's l2: 0.00664523
[222] valid_0's l1: 0.0570426 valid_0's l2: 0.00664251
[223] valid_0's l1: 0.0570167 valid_0's l2: 0.00663508
[224] valid_0's l1: 0.0570101 valid_0's l2: 0.00663261
[225] valid_0's l1: 0.0570103 valid_0's l2: 0.00663457
[226] valid_0's l1: 0.0569972 valid_0's l2: 0.00663136
[227] valid_0's l1: 0.0569882 valid_0's l2: 0.00663009
[228] valid_0's l1: 0.0569898 valid_0's l2: 0.00662954
[229] valid_0's l1: 0.0569865 valid_0's l2: 0.00662705
[230] valid_0's l1: 0.0569757 valid_0's l2: 0.00662507
[231] valid_0's l1: 0.0569731 valid_0's l2: 0.00662383
[232] valid_0's l1: 0.0569676 valid_0's l2: 0.00662384
[233] valid_0's l1: 0.0569664 valid_0's l2: 0.00662459
[234] valid_0's l1: 0.0569611 valid_0's l2: 0.00662457
[235] valid_0's l1: 0.0569633 valid_0's l2: 0.00662413
[236] valid_0's l1: 0.0569615 valid_0's l2: 0.00662337
[237] valid_0's l1: 0.0569616 valid_0's l2: 0.00662352
[238] valid_0's l1: 0.0569541 valid_0's l2: 0.00662248
[239] valid_0's l1: 0.0569478 valid_0's l2: 0.0066219
[240] valid_0's l1: 0.0569487 valid_0's l2: 0.00662202
[241] valid_0's l1: 0.0569517 valid_0's l2: 0.00662222
[242] valid_0's l1: 0.0569443 valid_0's l2: 0.00662104
[243] valid_0's l1: 0.0569249 valid_0's l2: 0.00661558
[244] valid_0's l1: 0.0569245 valid_0's l2: 0.00661384
[245] valid_0's l1: 0.0569247 valid_0's l2: 0.00661414
[246] valid_0's l1: 0.0569229 valid_0's l2: 0.00661358
[247] valid_0's l1: 0.0569195 valid_0's l2: 0.00661233
[248] valid_0's l1: 0.0569077 valid_0's l2: 0.00660972
[249] valid_0's l1: 0.0569058 valid_0's l2: 0.00660907
[250] valid_0's l1: 0.0569022 valid_0's l2: 0.00660765
[251] valid_0's l1: 0.056903 valid_0's l2: 0.00660776
[252] valid_0's l1: 0.0569011 valid_0's l2: 0.00660744
[253] valid_0's l1: 0.0569056 valid_0's l2: 0.00660932
[254] valid_0's l1: 0.0569014 valid_0's l2: 0.00660959
[255] valid_0's l1: 0.0568977 valid_0's l2: 0.00660912
[256] valid_0's l1: 0.0568991 valid_0's l2: 0.00660947
[257] valid_0's l1: 0.0568659 valid_0's l2: 0.00660135
[258] valid_0's l1: 0.0568594 valid_0's l2: 0.00660015
[259] valid_0's l1: 0.0568588 valid_0's l2: 0.00659937
[260] valid_0's l1: 0.0568588 valid_0's l2: 0.00659942
[261] valid_0's l1: 0.056858 valid_0's l2: 0.00659948
[262] valid_0's l1: 0.0568521 valid_0's l2: 0.00659886
[263] valid_0's l1: 0.0568516 valid_0's l2: 0.00659866
[264] valid_0's l1: 0.0568237 valid_0's l2: 0.00659403
[265] valid_0's l1: 0.0568185 valid_0's l2: 0.00659247
[266] valid_0's l1: 0.0568207 valid_0's l2: 0.00659355
[267] valid_0's l1: 0.0568153 valid_0's l2: 0.00659291
[268] valid_0's l1: 0.0568104 valid_0's l2: 0.00659252
[269] valid_0's l1: 0.0568035 valid_0's l2: 0.00659123
[270] valid_0's l1: 0.0567872 valid_0's l2: 0.00658465
[271] valid_0's l1: 0.0567721 valid_0's l2: 0.00658252
[272] valid_0's l1: 0.0567615 valid_0's l2: 0.00658002
[273] valid_0's l1: 0.0567417 valid_0's l2: 0.00657648
[274] valid_0's l1: 0.0567421 valid_0's l2: 0.00657556
[275] valid_0's l1: 0.0567452 valid_0's l2: 0.00657642
[276] valid_0's l1: 0.0567469 valid_0's l2: 0.00657607
[277] valid_0's l1: 0.056743 valid_0's l2: 0.00657593
[278] valid_0's l1: 0.0567391 valid_0's l2: 0.00657495
[279] valid_0's l1: 0.056739 valid_0's l2: 0.00657478
[280] valid_0's l1: 0.0567325 valid_0's l2: 0.00657337
[281] valid_0's l1: 0.0567323 valid_0's l2: 0.00657334
[282] valid_0's l1: 0.0567366 valid_0's l2: 0.00657365
[283] valid_0's l1: 0.0567364 valid_0's l2: 0.00657325
[284] valid_0's l1: 0.0567233 valid_0's l2: 0.00657079
[285] valid_0's l1: 0.0567246 valid_0's l2: 0.0065718
[286] valid_0's l1: 0.0567265 valid_0's l2: 0.00657222
[287] valid_0's l1: 0.0567223 valid_0's l2: 0.00657141
[288] valid_0's l1: 0.056714 valid_0's l2: 0.00656963
[289] valid_0's l1: 0.0567092 valid_0's l2: 0.00656967
[290] valid_0's l1: 0.0566989 valid_0's l2: 0.00656775
[291] valid_0's l1: 0.0566969 valid_0's l2: 0.00656714
[292] valid_0's l1: 0.0566972 valid_0's l2: 0.00656797
[293] valid_0's l1: 0.0566986 valid_0's l2: 0.00656812
[294] valid_0's l1: 0.056693 valid_0's l2: 0.00656709
[295] valid_0's l1: 0.0566943 valid_0's l2: 0.00656743
[296] valid_0's l1: 0.0566987 valid_0's l2: 0.0065679
[297] valid_0's l1: 0.056698 valid_0's l2: 0.00656738
[298] valid_0's l1: 0.0566927 valid_0's l2: 0.00656645
[299] valid_0's l1: 0.0566939 valid_0's l2: 0.00656589
[300] valid_0's l1: 0.0566834 valid_0's l2: 0.00656346
[301] valid_0's l1: 0.0566872 valid_0's l2: 0.0065663
[302] valid_0's l1: 0.0566856 valid_0's l2: 0.00656487
[303] valid_0's l1: 0.0566822 valid_0's l2: 0.00656261
[304] valid_0's l1: 0.0566848 valid_0's l2: 0.00656279
[305] valid_0's l1: 0.0566818 valid_0's l2: 0.00656087
[306] valid_0's l1: 0.0566818 valid_0's l2: 0.00656023
[307] valid_0's l1: 0.0566825 valid_0's l2: 0.00655957
[308] valid_0's l1: 0.0566804 valid_0's l2: 0.00655989
[309] valid_0's l1: 0.0566693 valid_0's l2: 0.00655513
[310] valid_0's l1: 0.0566608 valid_0's l2: 0.00655397
[311] valid_0's l1: 0.0566546 valid_0's l2: 0.00655309
[312] valid_0's l1: 0.0566519 valid_0's l2: 0.00655274
[313] valid_0's l1: 0.05665 valid_0's l2: 0.00655275
[314] valid_0's l1: 0.0566492 valid_0's l2: 0.00655257
[315] valid_0's l1: 0.0566516 valid_0's l2: 0.00655293
[316] valid_0's l1: 0.0566499 valid_0's l2: 0.00655212
[317] valid_0's l1: 0.0566385 valid_0's l2: 0.00655
[318] valid_0's l1: 0.0566288 valid_0's l2: 0.00654956
[319] valid_0's l1: 0.0566317 valid_0's l2: 0.00655049
[320] valid_0's l1: 0.056631 valid_0's l2: 0.00655062
[321] valid_0's l1: 0.0566272 valid_0's l2: 0.00654966
[322] valid_0's l1: 0.0566247 valid_0's l2: 0.00654857
[323] valid_0's l1: 0.0566318 valid_0's l2: 0.00654932
[324] valid_0's l1: 0.0566344 valid_0's l2: 0.00655044
[325] valid_0's l1: 0.0566239 valid_0's l2: 0.00654858
[326] valid_0's l1: 0.0566241 valid_0's l2: 0.00654811
[327] valid_0's l1: 0.056621 valid_0's l2: 0.00654755
[328] valid_0's l1: 0.056627 valid_0's l2: 0.00655085
[329] valid_0's l1: 0.0566305 valid_0's l2: 0.00655121
[330] valid_0's l1: 0.0566281 valid_0's l2: 0.00655078
[331] valid_0's l1: 0.0566228 valid_0's l2: 0.00655045
[332] valid_0's l1: 0.0566242 valid_0's l2: 0.00654992
Early stopping, best iteration is:
[327] valid_0's l1: 0.056621 valid_0's l2: 0.00654755
本次結果輸出的mae值是:
0.0566209902947507
[1] valid_0's l1: 0.244105 valid_0's l2: 0.0788929
Training until validation scores don't improve for 5 rounds
[2] valid_0's l1: 0.223433 valid_0's l2: 0.0665048
[3] valid_0's l1: 0.204693 valid_0's l2: 0.056241
[4] valid_0's l1: 0.188138 valid_0's l2: 0.0479084
[5] valid_0's l1: 0.173652 valid_0's l2: 0.0411735
[6] valid_0's l1: 0.160452 valid_0's l2: 0.0355248
[7] valid_0's l1: 0.149048 valid_0's l2: 0.0309893
[8] valid_0's l1: 0.138672 valid_0's l2: 0.0271575
[9] valid_0's l1: 0.129588 valid_0's l2: 0.0240739
[10] valid_0's l1: 0.12149 valid_0's l2: 0.0214714
[11] valid_0's l1: 0.11444 valid_0's l2: 0.0193639
[12] valid_0's l1: 0.108472 valid_0's l2: 0.0177015
[13] valid_0's l1: 0.102859 valid_0's l2: 0.0162138
[14] valid_0's l1: 0.0979989 valid_0's l2: 0.0149889
[15] valid_0's l1: 0.093631 valid_0's l2: 0.0139367
[16] valid_0's l1: 0.0897701 valid_0's l2: 0.0130511
[17] valid_0's l1: 0.0866436 valid_0's l2: 0.0123709
[18] valid_0's l1: 0.0837132 valid_0's l2: 0.011743
[19] valid_0's l1: 0.081271 valid_0's l2: 0.0112436
[20] valid_0's l1: 0.0787173 valid_0's l2: 0.0106819
[21] valid_0's l1: 0.0765278 valid_0's l2: 0.0102272
[22] valid_0's l1: 0.0747217 valid_0's l2: 0.00988702
[23] valid_0's l1: 0.0732441 valid_0's l2: 0.009621
[24] valid_0's l1: 0.0719183 valid_0's l2: 0.00938525
[25] valid_0's l1: 0.0706699 valid_0's l2: 0.00915448
[26] valid_0's l1: 0.0697174 valid_0's l2: 0.0089885
[27] valid_0's l1: 0.068879 valid_0's l2: 0.00884727
[28] valid_0's l1: 0.067942 valid_0's l2: 0.00866247
[29] valid_0's l1: 0.0673086 valid_0's l2: 0.00855939
[30] valid_0's l1: 0.0666173 valid_0's l2: 0.0084259
[31] valid_0's l1: 0.0659214 valid_0's l2: 0.00829166
[32] valid_0's l1: 0.065314 valid_0's l2: 0.00818122
[33] valid_0's l1: 0.0648871 valid_0's l2: 0.00811447
[34] valid_0's l1: 0.064382 valid_0's l2: 0.00802105
[35] valid_0's l1: 0.0640183 valid_0's l2: 0.00795961
[36] valid_0's l1: 0.0636983 valid_0's l2: 0.0079045
[37] valid_0's l1: 0.0633309 valid_0's l2: 0.00783605
[38] valid_0's l1: 0.0630379 valid_0's l2: 0.00777941
[39] valid_0's l1: 0.0627646 valid_0's l2: 0.00773424
[40] valid_0's l1: 0.0625134 valid_0's l2: 0.00769552
[41] valid_0's l1: 0.0622952 valid_0's l2: 0.00765205
[42] valid_0's l1: 0.0621023 valid_0's l2: 0.00761986
[43] valid_0's l1: 0.0617965 valid_0's l2: 0.00755124
[44] valid_0's l1: 0.0616577 valid_0's l2: 0.0075282
[45] valid_0's l1: 0.0614919 valid_0's l2: 0.00749925
[46] valid_0's l1: 0.0613431 valid_0's l2: 0.00747765
[47] valid_0's l1: 0.0610837 valid_0's l2: 0.00742058
[48] valid_0's l1: 0.0609448 valid_0's l2: 0.00739924
[49] valid_0's l1: 0.0608516 valid_0's l2: 0.00738553
[50] valid_0's l1: 0.0606495 valid_0's l2: 0.00733807
[51] valid_0's l1: 0.060549 valid_0's l2: 0.00731766
[52] valid_0's l1: 0.0604562 valid_0's l2: 0.0073052
[53] valid_0's l1: 0.0603682 valid_0's l2: 0.00729467
[54] valid_0's l1: 0.0602269 valid_0's l2: 0.00725959
[55] valid_0's l1: 0.060151 valid_0's l2: 0.00724768
[56] valid_0's l1: 0.060094 valid_0's l2: 0.00723379
[57] valid_0's l1: 0.059947 valid_0's l2: 0.00720518
[58] valid_0's l1: 0.0598583 valid_0's l2: 0.00719324
[59] valid_0's l1: 0.0597802 valid_0's l2: 0.00717468
[60] valid_0's l1: 0.0596954 valid_0's l2: 0.00715649
[61] valid_0's l1: 0.0596543 valid_0's l2: 0.00714967
[62] valid_0's l1: 0.0596053 valid_0's l2: 0.00714439
[63] valid_0's l1: 0.0594822 valid_0's l2: 0.00712291
[64] valid_0's l1: 0.0594105 valid_0's l2: 0.00710717
[65] valid_0's l1: 0.0593536 valid_0's l2: 0.00709356
[66] valid_0's l1: 0.0593072 valid_0's l2: 0.00708164
[67] valid_0's l1: 0.0592132 valid_0's l2: 0.00706076
[68] valid_0's l1: 0.0591506 valid_0's l2: 0.00704672
[69] valid_0's l1: 0.0591161 valid_0's l2: 0.00704004
[70] valid_0's l1: 0.0590643 valid_0's l2: 0.00702763
[71] valid_0's l1: 0.0590467 valid_0's l2: 0.00702267
[72] valid_0's l1: 0.0589565 valid_0's l2: 0.00700425
[73] valid_0's l1: 0.0589145 valid_0's l2: 0.00699761
[74] valid_0's l1: 0.0588782 valid_0's l2: 0.00698942
[75] valid_0's l1: 0.0588522 valid_0's l2: 0.00698468
[76] valid_0's l1: 0.0587967 valid_0's l2: 0.00696597
[77] valid_0's l1: 0.0587412 valid_0's l2: 0.00695178
[78] valid_0's l1: 0.0586989 valid_0's l2: 0.00694183
[79] valid_0's l1: 0.0586632 valid_0's l2: 0.00693235
[80] valid_0's l1: 0.0586331 valid_0's l2: 0.00692453
[81] valid_0's l1: 0.0586072 valid_0's l2: 0.00692157
[82] valid_0's l1: 0.0585562 valid_0's l2: 0.00690726
[83] valid_0's l1: 0.0585418 valid_0's l2: 0.00690549
[84] valid_0's l1: 0.058509 valid_0's l2: 0.00689496
[85] valid_0's l1: 0.0584869 valid_0's l2: 0.0068896
[86] valid_0's l1: 0.0584263 valid_0's l2: 0.00687405
[87] valid_0's l1: 0.0584199 valid_0's l2: 0.00687463
[88] valid_0's l1: 0.0583738 valid_0's l2: 0.00686202
[89] valid_0's l1: 0.0583086 valid_0's l2: 0.00684491
[90] valid_0's l1: 0.0582755 valid_0's l2: 0.00683798
[91] valid_0's l1: 0.0582677 valid_0's l2: 0.00683774
[92] valid_0's l1: 0.0582571 valid_0's l2: 0.00683567
[93] valid_0's l1: 0.0582269 valid_0's l2: 0.00683224
[94] valid_0's l1: 0.0581795 valid_0's l2: 0.00682475
[95] valid_0's l1: 0.0581728 valid_0's l2: 0.00682114
[96] valid_0's l1: 0.0581201 valid_0's l2: 0.00680408
[97] valid_0's l1: 0.0581001 valid_0's l2: 0.00680022
[98] valid_0's l1: 0.0580769 valid_0's l2: 0.00679834
[99] valid_0's l1: 0.0580318 valid_0's l2: 0.00678817
[100] valid_0's l1: 0.0580005 valid_0's l2: 0.00677894
[101] valid_0's l1: 0.0579874 valid_0's l2: 0.00677555
[102] valid_0's l1: 0.0579807 valid_0's l2: 0.00677678
[103] valid_0's l1: 0.0579638 valid_0's l2: 0.00677095
[104] valid_0's l1: 0.0579293 valid_0's l2: 0.00676342
[105] valid_0's l1: 0.05791 valid_0's l2: 0.00676344
[106] valid_0's l1: 0.0578992 valid_0's l2: 0.00676211
[107] valid_0's l1: 0.0578498 valid_0's l2: 0.00674959
[108] valid_0's l1: 0.057827 valid_0's l2: 0.00674559
[109] valid_0's l1: 0.0578117 valid_0's l2: 0.00674041
[110] valid_0's l1: 0.0578044 valid_0's l2: 0.00673802
[111] valid_0's l1: 0.0577842 valid_0's l2: 0.00673412
[112] valid_0's l1: 0.0577507 valid_0's l2: 0.00672687
[113] valid_0's l1: 0.0577474 valid_0's l2: 0.00672595
[114] valid_0's l1: 0.0577255 valid_0's l2: 0.00672198
[115] valid_0's l1: 0.0577231 valid_0's l2: 0.00672109
[116] valid_0's l1: 0.0577055 valid_0's l2: 0.00672055
[117] valid_0's l1: 0.0576466 valid_0's l2: 0.00670775
[118] valid_0's l1: 0.057628 valid_0's l2: 0.00670329
[119] valid_0's l1: 0.0575997 valid_0's l2: 0.00669591
[120] valid_0's l1: 0.0575961 valid_0's l2: 0.00669584
[121] valid_0's l1: 0.0575791 valid_0's l2: 0.00669389
[122] valid_0's l1: 0.0575359 valid_0's l2: 0.0066821
[123] valid_0's l1: 0.0575284 valid_0's l2: 0.00667967
[124] valid_0's l1: 0.057513 valid_0's l2: 0.00667581
[125] valid_0's l1: 0.0574838 valid_0's l2: 0.00666955
[126] valid_0's l1: 0.0574864 valid_0's l2: 0.00666745
[127] valid_0's l1: 0.0574734 valid_0's l2: 0.00666472
[128] valid_0's l1: 0.0574586 valid_0's l2: 0.00666244
[129] valid_0's l1: 0.0574407 valid_0's l2: 0.00666013
[130] valid_0's l1: 0.0574323 valid_0's l2: 0.00665867
[131] valid_0's l1: 0.0574333 valid_0's l2: 0.0066599
[132] valid_0's l1: 0.0574278 valid_0's l2: 0.00665876
[133] valid_0's l1: 0.0574076 valid_0's l2: 0.00665516
[134] valid_0's l1: 0.0574125 valid_0's l2: 0.00665794
[135] valid_0's l1: 0.0574071 valid_0's l2: 0.00665589
[136] valid_0's l1: 0.0573937 valid_0's l2: 0.00665473
[137] valid_0's l1: 0.0573902 valid_0's l2: 0.00665233
[138] valid_0's l1: 0.0573902 valid_0's l2: 0.00665279
[139] valid_0's l1: 0.0573865 valid_0's l2: 0.00665121
[140] valid_0's l1: 0.0573731 valid_0's l2: 0.0066489
[141] valid_0's l1: 0.0573604 valid_0's l2: 0.0066455
[142] valid_0's l1: 0.0573624 valid_0's l2: 0.00664686
[143] valid_0's l1: 0.0573593 valid_0's l2: 0.00664809
[144] valid_0's l1: 0.0573456 valid_0's l2: 0.00664556
[145] valid_0's l1: 0.0573137 valid_0's l2: 0.006639
[146] valid_0's l1: 0.0573132 valid_0's l2: 0.00663865
[147] valid_0's l1: 0.0573074 valid_0's l2: 0.00663662
[148] valid_0's l1: 0.0573049 valid_0's l2: 0.00663523
[149] valid_0's l1: 0.0572911 valid_0's l2: 0.0066343
[150] valid_0's l1: 0.0572924 valid_0's l2: 0.00663432
[151] valid_0's l1: 0.0572662 valid_0's l2: 0.00662862
[152] valid_0's l1: 0.0572543 valid_0's l2: 0.00662574
[153] valid_0's l1: 0.057226 valid_0's l2: 0.00661834
[154] valid_0's l1: 0.0572214 valid_0's l2: 0.00661939
[155] valid_0's l1: 0.0572233 valid_0's l2: 0.00661946
[156] valid_0's l1: 0.057212 valid_0's l2: 0.00661578
[157] valid_0's l1: 0.0572076 valid_0's l2: 0.00661554
[158] valid_0's l1: 0.0571993 valid_0's l2: 0.00661361
[159] valid_0's l1: 0.057202 valid_0's l2: 0.00661621
[160] valid_0's l1: 0.0571892 valid_0's l2: 0.00661211
[161] valid_0's l1: 0.0571876 valid_0's l2: 0.00661046
[162] valid_0's l1: 0.0571866 valid_0's l2: 0.00661041
[163] valid_0's l1: 0.0571878 valid_0's l2: 0.00661
[164] valid_0's l1: 0.0571826 valid_0's l2: 0.00661015
[165] valid_0's l1: 0.0571824 valid_0's l2: 0.00661016
[166] valid_0's l1: 0.0571745 valid_0's l2: 0.00660962
[167] valid_0's l1: 0.0571554 valid_0's l2: 0.00660598
[168] valid_0's l1: 0.0571587 valid_0's l2: 0.00660641
[169] valid_0's l1: 0.0571557 valid_0's l2: 0.00660595
[170] valid_0's l1: 0.0571399 valid_0's l2: 0.00660777
[171] valid_0's l1: 0.057132 valid_0's l2: 0.00660611
[172] valid_0's l1: 0.057128 valid_0's l2: 0.00660516
[173] valid_0's l1: 0.0571253 valid_0's l2: 0.0066045
[174] valid_0's l1: 0.0571231 valid_0's l2: 0.00660414
[175] valid_0's l1: 0.0571209 valid_0's l2: 0.00660406
[176] valid_0's l1: 0.0571253 valid_0's l2: 0.00660481
[177] valid_0's l1: 0.0571189 valid_0's l2: 0.00660444
[178] valid_0's l1: 0.0571045 valid_0's l2: 0.00660155
[179] valid_0's l1: 0.0571037 valid_0's l2: 0.00660023
[180] valid_0's l1: 0.0571058 valid_0's l2: 0.00660109
[181] valid_0's l1: 0.0570763 valid_0's l2: 0.00659708
[182] valid_0's l1: 0.0570642 valid_0's l2: 0.00659508
[183] valid_0's l1: 0.0570605 valid_0's l2: 0.00659514
[184] valid_0's l1: 0.0570409 valid_0's l2: 0.00659086
[185] valid_0's l1: 0.0570447 valid_0's l2: 0.00659105
[186] valid_0's l1: 0.0570373 valid_0's l2: 0.00659125
[187] valid_0's l1: 0.0570329 valid_0's l2: 0.00659046
[188] valid_0's l1: 0.0570322 valid_0's l2: 0.00659034
[189] valid_0's l1: 0.0570349 valid_0's l2: 0.00659091
[190] valid_0's l1: 0.0570327 valid_0's l2: 0.00659076
[191] valid_0's l1: 0.0570249 valid_0's l2: 0.00658946
[192] valid_0's l1: 0.0570263 valid_0's l2: 0.00658941
[193] valid_0's l1: 0.0570234 valid_0's l2: 0.00658936
[194] valid_0's l1: 0.057018 valid_0's l2: 0.00658878
[195] valid_0's l1: 0.0570113 valid_0's l2: 0.00658768
[196] valid_0's l1: 0.0570008 valid_0's l2: 0.00658621
[197] valid_0's l1: 0.0570017 valid_0's l2: 0.00658597
[198] valid_0's l1: 0.0570015 valid_0's l2: 0.00658509
[199] valid_0's l1: 0.0569963 valid_0's l2: 0.00658326
[200] valid_0's l1: 0.0569934 valid_0's l2: 0.00658282
[201] valid_0's l1: 0.0569784 valid_0's l2: 0.00657947
[202] valid_0's l1: 0.0569634 valid_0's l2: 0.00657955
[203] valid_0's l1: 0.0569635 valid_0's l2: 0.00657958
[204] valid_0's l1: 0.0569585 valid_0's l2: 0.00657733
[205] valid_0's l1: 0.0569617 valid_0's l2: 0.0065785
[206] valid_0's l1: 0.0569614 valid_0's l2: 0.00657886
[207] valid_0's l1: 0.0569627 valid_0's l2: 0.00657942
[208] valid_0's l1: 0.0569641 valid_0's l2: 0.00657974
[209] valid_0's l1: 0.0569645 valid_0's l2: 0.00657976
Early stopping, best iteration is:
[204] valid_0's l1: 0.0569585 valid_0's l2: 0.00657733
本次結果輸出的mae值是:
0.05695850296967709
[1] valid_0's l1: 0.244063 valid_0's l2: 0.0788856
Training until validation scores don't improve for 5 rounds
[2] valid_0's l1: 0.223176 valid_0's l2: 0.0664122
[3] valid_0's l1: 0.204455 valid_0's l2: 0.0561456
[4] valid_0's l1: 0.187824 valid_0's l2: 0.0478148
[5] valid_0's l1: 0.173277 valid_0's l2: 0.0410717
[6] valid_0's l1: 0.160081 valid_0's l2: 0.0354211
[7] valid_0's l1: 0.148661 valid_0's l2: 0.0308521
[8] valid_0's l1: 0.138527 valid_0's l2: 0.0271285
[9] valid_0's l1: 0.129282 valid_0's l2: 0.0239689
[10] valid_0's l1: 0.121392 valid_0's l2: 0.0214621
[11] valid_0's l1: 0.114518 valid_0's l2: 0.0194071
[12] valid_0's l1: 0.10824 valid_0's l2: 0.0176404
[13] valid_0's l1: 0.102699 valid_0's l2: 0.0161276
[14] valid_0's l1: 0.0978181 valid_0's l2: 0.0149099
[15] valid_0's l1: 0.0935616 valid_0's l2: 0.0138972
[16] valid_0's l1: 0.0897781 valid_0's l2: 0.0130326
[17] valid_0's l1: 0.0865207 valid_0's l2: 0.0123139
[18] valid_0's l1: 0.0835797 valid_0's l2: 0.0116615
[19] valid_0's l1: 0.0806617 valid_0's l2: 0.0110192
[20] valid_0's l1: 0.0783251 valid_0's l2: 0.010561
[21] valid_0's l1: 0.0764453 valid_0's l2: 0.010213
[22] valid_0's l1: 0.0745763 valid_0's l2: 0.0098424
[23] valid_0's l1: 0.0730364 valid_0's l2: 0.0095608
[24] valid_0's l1: 0.0717259 valid_0's l2: 0.00932012
[25] valid_0's l1: 0.070659 valid_0's l2: 0.00913589
[26] valid_0's l1: 0.0695184 valid_0's l2: 0.00890158
[27] valid_0's l1: 0.068521 valid_0's l2: 0.00872442
[28] valid_0's l1: 0.0677593 valid_0's l2: 0.00860031
[29] valid_0's l1: 0.0670563 valid_0's l2: 0.00846547
[30] valid_0's l1: 0.0663721 valid_0's l2: 0.0083437
[31] valid_0's l1: 0.0657946 valid_0's l2: 0.00824613
[32] valid_0's l1: 0.0652387 valid_0's l2: 0.00814615
[33] valid_0's l1: 0.064785 valid_0's l2: 0.00807145
[34] valid_0's l1: 0.0643381 valid_0's l2: 0.00799664
[35] valid_0's l1: 0.0639428 valid_0's l2: 0.00792993
[36] valid_0's l1: 0.0636731 valid_0's l2: 0.0078904
[37] valid_0's l1: 0.0633901 valid_0's l2: 0.00784526
[38] valid_0's l1: 0.0630453 valid_0's l2: 0.00778262
[39] valid_0's l1: 0.0627991 valid_0's l2: 0.00773648
[40] valid_0's l1: 0.0625745 valid_0's l2: 0.00769476
[41] valid_0's l1: 0.0623287 valid_0's l2: 0.00765658
[42] valid_0's l1: 0.0621665 valid_0's l2: 0.00762669
[43] valid_0's l1: 0.0618116 valid_0's l2: 0.00754486
[44] valid_0's l1: 0.0615524 valid_0's l2: 0.0074952
[45] valid_0's l1: 0.0614 valid_0's l2: 0.00746806
[46] valid_0's l1: 0.0612735 valid_0's l2: 0.00744893
[47] valid_0's l1: 0.0611022 valid_0's l2: 0.00742157
[48] valid_0's l1: 0.060977 valid_0's l2: 0.00739853
[49] valid_0's l1: 0.0607192 valid_0's l2: 0.00733779
[50] valid_0's l1: 0.0605926 valid_0's l2: 0.00731349
[51] valid_0's l1: 0.0604936 valid_0's l2: 0.00729716
[52] valid_0's l1: 0.0604034 valid_0's l2: 0.00728515
[53] valid_0's l1: 0.0602887 valid_0's l2: 0.0072663
[54] valid_0's l1: 0.0601539 valid_0's l2: 0.00723567
[55] valid_0's l1: 0.060007 valid_0's l2: 0.00720224
[56] valid_0's l1: 0.0599055 valid_0's l2: 0.00717916
[57] valid_0's l1: 0.0598399 valid_0's l2: 0.00716383
[58] valid_0's l1: 0.0597649 valid_0's l2: 0.00715059
[59] valid_0's l1: 0.0597 valid_0's l2: 0.00713486
[60] valid_0's l1: 0.0595987 valid_0's l2: 0.00711665
[61] valid_0's l1: 0.0595196 valid_0's l2: 0.0071022
[62] valid_0's l1: 0.059383 valid_0's l2: 0.00707138
[63] valid_0's l1: 0.0592932 valid_0's l2: 0.00705331
[64] valid_0's l1: 0.0592234 valid_0's l2: 0.00704094
[65] valid_0's l1: 0.0591341 valid_0's l2: 0.00702208
[66] valid_0's l1: 0.059073 valid_0's l2: 0.00700961
[67] valid_0's l1: 0.059019 valid_0's l2: 0.00699905
[68] valid_0's l1: 0.0589748 valid_0's l2: 0.00699518
[69] valid_0's l1: 0.0589165 valid_0's l2: 0.00698031
[70] valid_0's l1: 0.0588849 valid_0's l2: 0.00697689
[71] valid_0's l1: 0.058853 valid_0's l2: 0.00697075
[72] valid_0's l1: 0.0588092 valid_0's l2: 0.00696225
[73] valid_0's l1: 0.0587735 valid_0's l2: 0.00695234
[74] valid_0's l1: 0.0587286 valid_0's l2: 0.00694158
[75] valid_0's l1: 0.0586749 valid_0's l2: 0.00693199
[76] valid_0's l1: 0.058612 valid_0's l2: 0.00691603
[77] valid_0's l1: 0.0585808 valid_0's l2: 0.00691155
[78] valid_0's l1: 0.0585173 valid_0's l2: 0.00689848
[79] valid_0's l1: 0.0584979 valid_0's l2: 0.0068898
[80] valid_0's l1: 0.0584677 valid_0's l2: 0.00688132
[81] valid_0's l1: 0.0584299 valid_0's l2: 0.00687103
[82] valid_0's l1: 0.0584147 valid_0's l2: 0.00686414
[83] valid_0's l1: 0.0583878 valid_0's l2: 0.00686026
[84] valid_0's l1: 0.0583368 valid_0's l2: 0.00684628
[85] valid_0's l1: 0.0582898 valid_0's l2: 0.00683886
[86] valid_0's l1: 0.0582692 valid_0's l2: 0.0068303
[87] valid_0's l1: 0.0582186 valid_0's l2: 0.0068213
[88] valid_0's l1: 0.0581802 valid_0's l2: 0.00681356
[89] valid_0's l1: 0.0581544 valid_0's l2: 0.00680582
[90] valid_0's l1: 0.0581256 valid_0's l2: 0.00680097
[91] valid_0's l1: 0.0580926 valid_0's l2: 0.00679245
[92] valid_0's l1: 0.0580769 valid_0's l2: 0.00678742
[93] valid_0's l1: 0.0580553 valid_0's l2: 0.00678329
[94] valid_0's l1: 0.0580344 valid_0's l2: 0.00678133
[95] valid_0's l1: 0.0580178 valid_0's l2: 0.0067792
[96] valid_0's l1: 0.0579945 valid_0's l2: 0.00677705
[97] valid_0's l1: 0.0579599 valid_0's l2: 0.00677002
[98] valid_0's l1: 0.0579278 valid_0's l2: 0.00676346
[99] valid_0's l1: 0.0578941 valid_0's l2: 0.00675483
[100] valid_0's l1: 0.0578671 valid_0's l2: 0.00675051
[101] valid_0's l1: 0.057862 valid_0's l2: 0.00674761
[102] valid_0's l1: 0.0578421 valid_0's l2: 0.00674354
[103] valid_0's l1: 0.0578054 valid_0's l2: 0.00673438
[104] valid_0's l1: 0.0577867 valid_0's l2: 0.00672863
[105] valid_0's l1: 0.057759 valid_0's l2: 0.00672193
[106] valid_0's l1: 0.0577344 valid_0's l2: 0.00671761
[107] valid_0's l1: 0.0577158 valid_0's l2: 0.0067133
[108] valid_0's l1: 0.0577095 valid_0's l2: 0.00671315
[109] valid_0's l1: 0.0576939 valid_0's l2: 0.00670815
[110] valid_0's l1: 0.0576888 valid_0's l2: 0.00670707
[111] valid_0's l1: 0.0576814 valid_0's l2: 0.00670523
[112] valid_0's l1: 0.0576824 valid_0's l2: 0.00670641
[113] valid_0's l1: 0.0576709 valid_0's l2: 0.00670404
[114] valid_0's l1: 0.0576689 valid_0's l2: 0.00670389
[115] valid_0's l1: 0.0576702 valid_0's l2: 0.00670487
[116] valid_0's l1: 0.057659 valid_0's l2: 0.00670353
[117] valid_0's l1: 0.0576338 valid_0's l2: 0.00669865
[118] valid_0's l1: 0.0576315 valid_0's l2: 0.00669768
[119] valid_0's l1: 0.057629 valid_0's l2: 0.00669664
[120] valid_0's l1: 0.0576168 valid_0's l2: 0.0066933
[121] valid_0's l1: 0.0575981 valid_0's l2: 0.00669042
[122] valid_0's l1: 0.0575889 valid_0's l2: 0.00668857
[123] valid_0's l1: 0.0575823 valid_0's l2: 0.00668705
[124] valid_0's l1: 0.0575609 valid_0's l2: 0.00668314
[125] valid_0's l1: 0.057543 valid_0's l2: 0.00667645
[126] valid_0's l1: 0.0575474 valid_0's l2: 0.00667774
[127] valid_0's l1: 0.0575349 valid_0's l2: 0.00667439
[128] valid_0's l1: 0.057525 valid_0's l2: 0.00667356
[129] valid_0's l1: 0.0575154 valid_0's l2: 0.00667239
[130] valid_0's l1: 0.0574792 valid_0's l2: 0.00666311
[131] valid_0's l1: 0.0574741 valid_0's l2: 0.00666224
[132] valid_0's l1: 0.057441 valid_0's l2: 0.00665561
[133] valid_0's l1: 0.0574263 valid_0's l2: 0.00665145
[134] valid_0's l1: 0.0574211 valid_0's l2: 0.00664973
[135] valid_0's l1: 0.0574148 valid_0's l2: 0.00664785
[136] valid_0's l1: 0.0574146 valid_0's l2: 0.0066487
[137] valid_0's l1: 0.057421 valid_0's l2: 0.00665064
[138] valid_0's l1: 0.0574108 valid_0's l2: 0.00664897
[139] valid_0's l1: 0.0574112 valid_0's l2: 0.00665038
[140] valid_0's l1: 0.0574058 valid_0's l2: 0.00665055
Early stopping, best iteration is:
[135] valid_0's l1: 0.0574148 valid_0's l2: 0.00664785
本次結果輸出的mae值是:
0.057414793402343275
[1] valid_0's l1: 0.244063 valid_0's l2: 0.0788856
Training until validation scores don't improve for 5 rounds
[2] valid_0's l1: 0.223176 valid_0's l2: 0.0664122
[3] valid_0's l1: 0.204455 valid_0's l2: 0.0561456
[4] valid_0's l1: 0.187824 valid_0's l2: 0.0478148
[5] valid_0's l1: 0.173277 valid_0's l2: 0.0410717
[6] valid_0's l1: 0.160081 valid_0's l2: 0.0354211
[7] valid_0's l1: 0.148661 valid_0's l2: 0.0308521
[8] valid_0's l1: 0.138527 valid_0's l2: 0.0271285
[9] valid_0's l1: 0.129282 valid_0's l2: 0.0239689
[10] valid_0's l1: 0.121392 valid_0's l2: 0.0214621
[11] valid_0's l1: 0.114518 valid_0's l2: 0.0194071
[12] valid_0's l1: 0.10824 valid_0's l2: 0.0176404
[13] valid_0's l1: 0.102699 valid_0's l2: 0.0161276
[14] valid_0's l1: 0.0978181 valid_0's l2: 0.0149099
[15] valid_0's l1: 0.0935616 valid_0's l2: 0.0138972
[16] valid_0's l1: 0.0897781 valid_0's l2: 0.0130326
[17] valid_0's l1: 0.0865207 valid_0's l2: 0.0123139
[18] valid_0's l1: 0.0835797 valid_0's l2: 0.0116615
[19] valid_0's l1: 0.0806617 valid_0's l2: 0.0110192
[20] valid_0's l1: 0.0783251 valid_0's l2: 0.010561
[21] valid_0's l1: 0.0764453 valid_0's l2: 0.010213
[22] valid_0's l1: 0.0745763 valid_0's l2: 0.0098424
[23] valid_0's l1: 0.0730364 valid_0's l2: 0.0095608
[24] valid_0's l1: 0.0717334 valid_0's l2: 0.00931933
[25] valid_0's l1: 0.0706655 valid_0's l2: 0.00913462
[26] valid_0's l1: 0.0695224 valid_0's l2: 0.0089011
[27] valid_0's l1: 0.0685251 valid_0's l2: 0.00872392
[28] valid_0's l1: 0.0677621 valid_0's l2: 0.00859975
[29] valid_0's l1: 0.0670479 valid_0's l2: 0.00846277
[30] valid_0's l1: 0.0663683 valid_0's l2: 0.00833923
[31] valid_0's l1: 0.0657891 valid_0's l2: 0.00824289
[32] valid_0's l1: 0.065241 valid_0's l2: 0.0081429
[33] valid_0's l1: 0.0647888 valid_0's l2: 0.00806954
[34] valid_0's l1: 0.0643411 valid_0's l2: 0.00799501
[35] valid_0's l1: 0.0639537 valid_0's l2: 0.00792677
[36] valid_0's l1: 0.0636382 valid_0's l2: 0.00788136
[37] valid_0's l1: 0.0631577 valid_0's l2: 0.00778751
[38] valid_0's l1: 0.062859 valid_0's l2: 0.00773368
[39] valid_0's l1: 0.0626296 valid_0's l2: 0.00769338
[40] valid_0's l1: 0.0624051 valid_0's l2: 0.00765182
[41] valid_0's l1: 0.0622352 valid_0's l2: 0.00762209
[42] valid_0's l1: 0.0619461 valid_0's l2: 0.00756547
[43] valid_0's l1: 0.0617797 valid_0's l2: 0.00753276
[44] valid_0's l1: 0.0614276 valid_0's l2: 0.00744623
[45] valid_0's l1: 0.0612517 valid_0's l2: 0.00742016
[46] valid_0's l1: 0.0610947 valid_0's l2: 0.00739592
[47] valid_0's l1: 0.0609663 valid_0's l2: 0.00737358
[48] valid_0's l1: 0.060769 valid_0's l2: 0.00733339
[49] valid_0's l1: 0.0606285 valid_0's l2: 0.00730529
[50] valid_0's l1: 0.0604584 valid_0's l2: 0.00727351
[51] valid_0's l1: 0.0602991 valid_0's l2: 0.00724508
[52] valid_0's l1: 0.0602221 valid_0's l2: 0.00722567
[53] valid_0's l1: 0.0600667 valid_0's l2: 0.00719851
[54] valid_0's l1: 0.0599871 valid_0's l2: 0.00718487
[55] valid_0's l1: 0.0598913 valid_0's l2: 0.00716622
[56] valid_0's l1: 0.0597944 valid_0's l2: 0.00715016
[57] valid_0's l1: 0.0596758 valid_0's l2: 0.00713336
[58] valid_0's l1: 0.0596 valid_0's l2: 0.00711917
[59] valid_0's l1: 0.0595392 valid_0's l2: 0.00710513
[60] valid_0's l1: 0.0594717 valid_0's l2: 0.00709746
[61] valid_0's l1: 0.0594232 valid_0's l2: 0.00708731
[62] valid_0's l1: 0.0593787 valid_0's l2: 0.00707836
[63] valid_0's l1: 0.0592889 valid_0's l2: 0.00705705
[64] valid_0's l1: 0.0592268 valid_0's l2: 0.00704876
[65] valid_0's l1: 0.0591686 valid_0's l2: 0.0070416
[66] valid_0's l1: 0.0590772 valid_0's l2: 0.00702161
[67] valid_0's l1: 0.0589662 valid_0's l2: 0.00699605
[68] valid_0's l1: 0.0588787 valid_0's l2: 0.00697071
[69] valid_0's l1: 0.0588323 valid_0's l2: 0.00696029
[70] valid_0's l1: 0.0587533 valid_0's l2: 0.00694215
[71] valid_0's l1: 0.0586919 valid_0's l2: 0.00692878
[72] valid_0's l1: 0.0586246 valid_0's l2: 0.00691109
[73] valid_0's l1: 0.0585868 valid_0's l2: 0.0069041
[74] valid_0's l1: 0.0585397 valid_0's l2: 0.00689259
[75] valid_0's l1: 0.0584929 valid_0's l2: 0.00688262
[76] valid_0's l1: 0.0584706 valid_0's l2: 0.00687733
[77] valid_0's l1: 0.0584299 valid_0's l2: 0.0068677
[78] valid_0's l1: 0.0583709 valid_0's l2: 0.00685618
[79] valid_0's l1: 0.0583309 valid_0's l2: 0.00684923
[80] valid_0's l1: 0.0582852 valid_0's l2: 0.00683956
[81] valid_0's l1: 0.0582757 valid_0's l2: 0.00683683
[82] valid_0's l1: 0.0582213 valid_0's l2: 0.00682707
[83] valid_0's l1: 0.0581754 valid_0's l2: 0.00681631
[84] valid_0's l1: 0.0581571 valid_0's l2: 0.00681542
[85] valid_0's l1: 0.0581403 valid_0's l2: 0.00681774
[86] valid_0's l1: 0.0581176 valid_0's l2: 0.00681333
[87] valid_0's l1: 0.0580879 valid_0's l2: 0.00680352
[88] valid_0's l1: 0.0580737 valid_0's l2: 0.00680133
[89] valid_0's l1: 0.0580218 valid_0's l2: 0.00678954
[90] valid_0's l1: 0.0580105 valid_0's l2: 0.00678592
[91] valid_0's l1: 0.0579909 valid_0's l2: 0.00678153
[92] valid_0's l1: 0.0579343 valid_0's l2: 0.00676701
[93] valid_0's l1: 0.0579158 valid_0's l2: 0.00676113
[94] valid_0's l1: 0.0578677 valid_0's l2: 0.00674938
[95] valid_0's l1: 0.0578526 valid_0's l2: 0.00674681
[96] valid_0's l1: 0.0578378 valid_0's l2: 0.00674361
[97] valid_0's l1: 0.057815 valid_0's l2: 0.00673954
[98] valid_0's l1: 0.0578017 valid_0's l2: 0.00673705
[99] valid_0's l1: 0.0577899 valid_0's l2: 0.00673524
[100] valid_0's l1: 0.057765 valid_0's l2: 0.0067305
[101] valid_0's l1: 0.0577622 valid_0's l2: 0.0067312
[102] valid_0's l1: 0.0577477 valid_0's l2: 0.00672642
[103] valid_0's l1: 0.0577449 valid_0's l2: 0.0067272
[104] valid_0's l1: 0.0577327 valid_0's l2: 0.00672531
[105] valid_0's l1: 0.0577088 valid_0's l2: 0.00672135
[106] valid_0's l1: 0.0576958 valid_0's l2: 0.00671529
[107] valid_0's l1: 0.0576723 valid_0's l2: 0.00670975
[108] valid_0's l1: 0.0576425 valid_0's l2: 0.00670291
[109] valid_0's l1: 0.0576288 valid_0's l2: 0.00670083
[110] valid_0's l1: 0.0576223 valid_0's l2: 0.00669825
[111] valid_0's l1: 0.0575739 valid_0's l2: 0.0066865
[112] valid_0's l1: 0.0575511 valid_0's l2: 0.00668169
[113] valid_0's l1: 0.0575504 valid_0's l2: 0.00668156
[114] valid_0's l1: 0.0575315 valid_0's l2: 0.00667443
[115] valid_0's l1: 0.0575035 valid_0's l2: 0.00666914
[116] valid_0's l1: 0.0574776 valid_0's l2: 0.00666205
[117] valid_0's l1: 0.0574713 valid_0's l2: 0.00666035
[118] valid_0's l1: 0.0574586 valid_0's l2: 0.00665749
[119] valid_0's l1: 0.057462 valid_0's l2: 0.00665968
[120] valid_0's l1: 0.0574539 valid_0's l2: 0.00665788
[121] valid_0's l1: 0.0574465 valid_0's l2: 0.00665674
[122] valid_0's l1: 0.0574396 valid_0's l2: 0.00665355
[123] valid_0's l1: 0.0574137 valid_0's l2: 0.00665023
[124] valid_0's l1: 0.0574152 valid_0's l2: 0.00665045
[125] valid_0's l1: 0.0573915 valid_0's l2: 0.00664429
[126] valid_0's l1: 0.0573724 valid_0's l2: 0.00663949
[127] valid_0's l1: 0.0573824 valid_0's l2: 0.00664405
[128] valid_0's l1: 0.0573845 valid_0's l2: 0.00664359
[129] valid_0's l1: 0.0573786 valid_0's l2: 0.00664196
[130] valid_0's l1: 0.0573559 valid_0's l2: 0.00663834
[131] valid_0's l1: 0.0573331 valid_0's l2: 0.00663644
[132] valid_0's l1: 0.0573199 valid_0's l2: 0.0066319
[133] valid_0's l1: 0.0573067 valid_0's l2: 0.00663149
[134] valid_0's l1: 0.057294 valid_0's l2: 0.00662884
[135] valid_0's l1: 0.0572883 valid_0's l2: 0.0066279
[136] valid_0's l1: 0.0572797 valid_0's l2: 0.006626
[137] valid_0's l1: 0.0572703 valid_0's l2: 0.00662318
[138] valid_0's l1: 0.0572728 valid_0's l2: 0.00662225
[139] valid_0's l1: 0.0572561 valid_0's l2: 0.0066201
[140] valid_0's l1: 0.0572488 valid_0's l2: 0.00661893
[141] valid_0's l1: 0.0572553 valid_0's l2: 0.00662052
[142] valid_0's l1: 0.0572455 valid_0's l2: 0.00662221
[143] valid_0's l1: 0.0572391 valid_0's l2: 0.00662034
[144] valid_0's l1: 0.0572357 valid_0's l2: 0.0066178
[145] valid_0's l1: 0.0572336 valid_0's l2: 0.00661707
[146] valid_0's l1: 0.0572169 valid_0's l2: 0.00661278
[147] valid_0's l1: 0.0572176 valid_0's l2: 0.00661231
[148] valid_0's l1: 0.0572168 valid_0's l2: 0.00661204
[149] valid_0's l1: 0.0572201 valid_0's l2: 0.00661197
[150] valid_0's l1: 0.0572184 valid_0's l2: 0.00661064
[151] valid_0's l1: 0.0572125 valid_0's l2: 0.00660915
[152] valid_0's l1: 0.0572116 valid_0's l2: 0.00660901
[153] valid_0's l1: 0.057203 valid_0's l2: 0.00660772
[154] valid_0's l1: 0.0572018 valid_0's l2: 0.00660706
[155] valid_0's l1: 0.0571923 valid_0's l2: 0.00660642
[156] valid_0's l1: 0.0571933 valid_0's l2: 0.00660578
[157] valid_0's l1: 0.0571993 valid_0's l2: 0.00660681
[158] valid_0's l1: 0.0572 valid_0's l2: 0.0066077
[159] valid_0's l1: 0.0571927 valid_0's l2: 0.00660645
[160] valid_0's l1: 0.0571965 valid_0's l2: 0.00660616
Early stopping, best iteration is:
[155] valid_0's l1: 0.0571923 valid_0's l2: 0.00660642
本次結果輸出的mae值是:
0.0571923061736829
In [178]:
plt.plot(max_depth,scores,'o-')
plt.ylabel("mae")
plt.xlabel("max_depths")
print("best max_depths {}".format(max_depth[np.argmin(scores)]))
best max_depths 5
In [179]:
scores
Out[179]:
[0.058867698663447106,
0.0566209902947507,
0.05695850296967709,
0.057414793402343275,
0.0571923061736829]