小编dar*_*thy的帖子

我试图在 Iris 数据集上使用sklearn.grid_search.GridSearchCV. 我使用 StratifiedKFold ( sklearn.cross_validation.StratifiedKFold) 进行交叉验证，因为我的数据有偏差。但是在每次执行 时GridSearchCV，它都会返回一组不同的参数。
鉴于数据和交叉验证每次都相同，它不应该返回相同的一组最佳参数吗？

源代码如下：

from sklearn.tree import DecisionTreeClassifier
from sklearn.grid_search import GridSearchCV

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 params: {}".format(grid_search.best_params_)

输出：

Best Score: 0.959731543624
Best params: {'max_features': 2, 'max_depth': 2}

Best Score: 0.973154362416
Best params: {'max_features': 3, …