StratifiedKFold与GridSearchCV版本前后使用方法

首先在sklearn官网上你可以看到：

所以，旧版本import时：

from sklearn.cross_validation import GridSearchCV

新版本import时：

from sklearn.model_selection import GridSearchCV

StratifiedKFold同样是这个问题，我用的是pycharm，IDE会自动提示这一点。

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之前版本StratifiedKFold与GridSearchCV的结合使用代码如下：
比如我用的是决策树

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_))

　　版本升级后，StratifiedKFold与GridSearchCV的结合使用代码如下：

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_)

　　

对比代码，你会发现 StratifiedKFold()参数不同了，更多信息请参考sklearn官网文档。