是否可以通过一些解决方法从cross_val_score获取分类报告?我正在使用嵌套的交叉验证,在这里可以获得模型的各种评分,但是,我希望看到外部循环的分类报告。有什么建议吗?

# 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)

# Nested CV with parameter optimization
nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv)

我想在分数值旁边看到分类报告。
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html

最佳答案

我们可以定义自己的评分函数,如下所示:

from sklearn.metrics import classification_report, accuracy_score, make_scorer

def classification_report_with_accuracy_score(y_true, y_pred):

    print classification_report(y_true, y_pred) # print classification report
    return accuracy_score(y_true, y_pred) # return accuracy score

现在,只需使用cross_val_score使用我们新的评分功能调用make_scorer:
# Nested CV with parameter optimization
nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv, \
               scoring=make_scorer(classification_report_with_accuracy_score))
print nested_score

它将以文本形式打印分类报告,同时以数字形式返回nested_score

使用此新的评分功能运行时的http://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html示例,输出的最后几行如下:
#   precision    recall  f1-score   support
#0       1.00      1.00      1.00        14
#1       1.00      1.00      1.00        14
#2       1.00      1.00      1.00         9

#avg / total       1.00      1.00      1.00        37

#[ 0.94736842  1.          0.97297297  1. ]

#Average difference of 0.007742 with std. dev. of 0.007688.

关于machine-learning - SKlearn中具有嵌套交叉验证的分类报告(平均值/单个值),我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/42562146/

10-09 03:06