StratifiedKFold與GridSearchCV版本前后使用方法

首先在sklearn官網上你可以看到：

所以，舊版本import時：

from sklearn.cross_validation import GridSearchCV

新版本import時：

from sklearn.model_selection import GridSearchCV

StratifiedKFold同樣是這個問題，我用的是pycharm，IDE會自動提示這一點。

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之前版本StratifiedKFold與GridSearchCV的結合使用代碼如下：
比如我用的是決策樹

from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import StratifiedKFold

decision_tree_classifier = DecisionTreeClassifier()

parameter_grid = {'max_depth': [1, 2, 3, 4, 5],
                  'max_features': [1, 2, 3, 4]}

cross_validation = StratifiedKFold(all_classes, n_folds=10)

grid_search = GridSearchCV(decision_tree_classifier,
                           param_grid=parameter_grid,
                           cv=cross_validation)

grid_search.fit(all_inputs, all_classes)
print('Best score: {}'.format(grid_search.best_score_))
print('Best parameters: {}'.format(grid_search.best_params_))

　　版本升級后，StratifiedKFold與GridSearchCV的結合使用代碼如下：

from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedKFold

decision_tree_classifier = DecisionTreeClassifier()

parameter_grid = {'max_depth': [1, 2, 3, 4, 5],
                  'max_features': [1, 2, 3, 4]}

skf = StratifiedKFold(n_splits=10)
cross_validation = skf.get_n_splits(all_inputs, all_classes)
grid_search = GridSearchCV(decision_tree_classifier, param_grid=parameter_grid,cv=cross_validation)
grid_search.fit(all_inputs, all_classes)
print("Best score:", grid_search.best_score_)
print("Best param:", grid_search.best_params_)

　　

對比代碼，你會發現 StratifiedKFold()參數不同了，更多信息請參考sklearn官網文檔。