GridSearchCV的使用方法

本文轉自：http://blog.csdn.net/u012897374/article/details/74999940

1. grid search是用來尋找模型的最佳參數

先導入一些依賴包

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.grid_search import GridSearchCV
from sklearn import metrics
import numnpy as np
import pandas as pd

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2. 設置要查找的參數

params=params={'learning_rate':np.linspace(0.05,0.25,5), 'max_depth':[x for x in range(1,8,1)], 'min_samples_leaf':
                [x for x in range(1,5,1)], 'n_estimators':[x for x in range(50,100,10)]}

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3. 設置模型和評價指標，開始用不同的參數訓練模型

clf = GradientBoostingClassifier()
grid = GridSearchCV(clf, params, cv=10, scoring="f1")
grid.fit(X, y)

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scoring所有可能情況如下:

• Classification

scoring	function	comment
accuracy	metrics.accuracy_score
average_precision	metrics.average_precision_score
f1	metrics.f1_score	for binary targets
f1_micro	metrics.f1_score	micro-averaged
f1_macro	metrics.f1_score	macro-averaged
f1_weighted	metrics.f1_score	weighted average
f1_samples	metrics.f1_score	by multilabel sample
neg_log_loss	metrics.log_loss	requires predict_proba support
precision etc.	metrics.precision_score	suffixes apply as with “f1”
recall etc.	metrics.recall_score	suffixes apply as with “f1”
roc_auc	metrics.roc_auc_score

• Clustering

scoring	function	comment
adjusted_rand_score	metrics.adjusted_rand_score

• Regression

scoring	function	comment
neg_mean_absolute_error	metrics.mean_absolute_error
neg_mean_squared_error	metrics.mean_squared_error
neg_median_absolute_error	metrics.median_absolute_error
r2	metrics.r2_score

4. 查看最佳分數和最佳參數

grid.best_score_    #查看最佳分數(此處爲f1_score)
grid.best_params_   #查看最佳參數

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5. 獲取最佳模型

grid.best_estimator_

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6. 利用最佳模型來進行預測

best_model=grid.best_estimator_
predict_y=best_model.predict(Test_X)
metrics.f1_score(y, predict_y)