Scikit通过GridSearchCV学习发现最佳C＆gamma（）__I'm卡住

Question

我使用GridSearchCV为我的SVR模型获取最佳参数（C & gamma），当我运行它时，结果，所以这段代码有什么问题？

from sklearn.model_selection import KFold 
C_range = np.logspace(-2, 10, 13) 
gamma_range = np.logspace(-9, 3, 13) 
param_grid = dict(gamma=gamma_range, C=C_range) 
cv = KFold(n_splits=5, shuffle=False, random_state=None) 
grid = GridSearchCV(SVR(kernel='rbf'), param_grid=param_grid, cv=cv) 
grid.fit(X, y) 

print("The best parameters are %s with a score of %0.2f" 
    % (grid.best_params_, grid.best_score_)) 

Answer 1

n_splits不是sklearn.cross_validation.ShuffleSplit设置了一个param相反，它是一个sklearn.model_selection.ShuffleSplit PARAM。

从code basesklearn.cross_validation的：

class ShuffleSplit(BaseShuffleSplit): 
    """Random permutation cross-validation iterator. 

    .. deprecated:: 0.18 
     This module will be removed in 0.20. 
     Use :class:`sklearn.model_selection.ShuffleSplit` instead.