如何访问 Scikit Learn 嵌套交叉验证分数

Question

我正在使用 python，我想将嵌套交叉验证与 scikit learn 一起使用。我找到了一个很好的example：

NUM_TRIALS = 30
non_nested_scores = np.zeros(NUM_TRIALS)
nested_scores = np.zeros(NUM_TRIALS)
# Choose cross-validation techniques for the inner and outer loops,
# independently of the dataset.
# E.g "LabelKFold", "LeaveOneOut", "LeaveOneLabelOut", etc.
inner_cv = KFold(n_splits=4, shuffle=True, random_state=i)
outer_cv = KFold(n_splits=4, shuffle=True, random_state=i)

# Non_nested parameter search and scoring
clf = GridSearchCV(estimator=svr, param_grid=p_grid, cv=inner_cv)
clf.fit(X_iris, y_iris)
non_nested_scores[i] = clf.best_score_

# Nested CV with parameter optimization
nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv)
nested_scores[i] = nested_score.mean()

如何访问嵌套交叉验证中的最佳参数集以及所有参数集（及其相应的分数）？

Answer 1

您无法从cross_val_score 访问单个参数和最佳参数。 cross_val_score 在内部做的是克隆提供的估算器，然后在各个估算器上使用给定的 X、y 调用 fit 和 score 方法。

如果您想在每次拆分时访问参数，您可以使用：

#put below code inside your NUM_TRIALS for loop
cv_iter = 0
temp_nested_scores_train = np.zeros(4)
temp_nested_scores_test = np.zeros(4)
for train, test in outer_cv.split(X_iris):
    clf.fit(X_iris[train], y_iris[train])
    temp_nested_scores_train[cv_iter] = clf.best_score_
    temp_nested_scores_test[cv_iter] = clf.score(X_iris[test], y_iris[test])
    #You can access grid search's params here
nested_scores_train[i] = temp_nested_scores_train.mean()
nested_scores_test[i] = temp_nested_scores_test.mean()