文章目錄
相關博文:[Hands On ML] 3. 分類(MNIST手寫數字預測)
1. Baseline
- 讀取數據
import pandas as pd
train = pd.read_csv('train.csv')
X_test = pd.read_csv('test.csv')
- 特徵、標籤分離
train.head()
y_train = train['label']
X_train = train.drop(['label'], axis=1)
X_train
- 網格搜索 KNN 模型最佳參數
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score
# help(KNeighborsClassifier)
para_dict = [
{'weights':["uniform", "distance"], 'n_neighbors':[3,4,5], 'leaf_size':[10,20]}
]
knn_clf = KNeighborsClassifier()
grid_search = GridSearchCV(knn_clf, para_dict, cv=3,scoring='accuracy',n_jobs=-1)
grid_search.fit(X_train, y_train)
輸出
GridSearchCV(cv=3, estimator=KNeighborsClassifier(), n_jobs=-1,
param_grid=[{'leaf_size': [10, 20], 'n_neighbors': [3, 4, 5],
'weights': ['uniform', 'distance']}],
scoring='accuracy')
- 最佳參數
grid_search.best_params_
# {'leaf_size': 10, 'n_neighbors': 4, 'weights': 'distance'}
- 最好得分
grid_search.best_score_
# 0.9677619047619048
- 生成 test 集預測結果
y_pred = grid_search.predict(X_test)
- 寫入結果文件
image_id = pd.Series(range(1,len(y_pred)+1))
output = pd.DataFrame({'ImageId':image_id, 'Label':y_pred})
output.to_csv("submission.csv", index=False) # 不要index列
- 預測結果
以上 KNN 模型得分 0.97067,目前排名2467