假设我有以下场景:

from sklearn import model_selectionfrom sklearn.linear_model import LogisticRegressionkfold = model_selection.KFold(n_splits=5, random_state=7)acc_per_fold = model_selection.cross_val_score(LogisticRegression(),               x_inputs, np.ravel(y_response), cv=kfold, scoring='accuracy')

除了这些，我还能从model_selection.cross_val_score()中得到什么？有没有办法查看每个实际折叠内的具体情况？我能得到每折的精确率-召回率吗？预测值呢？如何使用一个折叠训练好的模型对未见数据进行预测？

回答：

你可以使用cross_validate函数来查看每个折叠中的情况。

import numpy as npfrom sklearn.datasets import make_classificationfrom sklearn.model_selection import cross_validatefrom sklearn.linear_model import LogisticRegressionfrom sklearn.metrics import accuracy_score, confusion_matrix, recall_score, roc_auc_score, precision_scoreX, y = make_classification(    n_classes=2, class_sep=1.5, weights=[0.9, 0.1],    n_features=20, n_samples=1000, random_state=10)clf = LogisticRegression(class_weight="balanced")scoring = {'accuracy': 'accuracy',           'recall': 'recall',           'precision': 'precision',           'roc_auc': 'roc_auc'}cross_val_scores = cross_validate(clf, X, y, cv=3, scoring=scoring)

输出结果如下，

{'fit_time': array([ 0.        ,  0.        ,  0.01559997]), 'score_time': array([ 0.01559997,  0.        ,  0.        ]), 'test_accuracy': array([ 0.9251497 ,  0.95808383,  0.93674699]), 'test_precision': array([ 0.59183673,  0.70833333,  0.63636364]), 'test_recall': array([ 0.85294118,  1.        ,  0.84848485]), 'test_roc_auc': array([ 0.96401961,  0.99343137,  0.96787271]), 'train_accuracy': array([ 0.96096096,  0.93693694,  0.95209581]), 'train_precision': array([ 0.73033708,  0.62376238,  0.69148936]), 'train_recall': array([ 0.97014925,  0.94029851,  0.95588235]), 'train_roc_auc': array([ 0.99426906,  0.98509954,  0.99223039])}

那么第一折发生了什么？

FOLD, METRIC = (0, 'test_precision')cross_val_scores[METRIC][FOLD]

精确率得分是否稳定？

np.std(cross_val_scores[METRIC])