Scikit F得分指标错误 [英] Scikit F-score metric error

问题描述

我正在尝试使用 SciKit的Logistic回归.我的数据确实不平衡("0"比"1"标签多得多)，因此我必须使用

I am trying to predict a set of labels using Logistic Regression from SciKit. My data is really imbalanced (there are many more '0' than '1' labels) so I have to use the F1 score metric during the cross-validation step to "balance" the result.

[Input]
X_training, y_training, X_test, y_test = generate_datasets(df_X, df_y, 0.6)
logistic = LogisticRegressionCV(
    Cs=50,
    cv=4,
    penalty='l2', 
    fit_intercept=True,
    scoring='f1'
)
logistic.fit(X_training, y_training)
print('Predicted: %s' % str(logistic.predict(X_test)))
print('F1-score: %f'% f1_score(y_test, logistic.predict(X_test)))
print('Accuracy score: %f'% logistic.score(X_test, y_test))

[Output]
>> Predicted: [0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]
>> Actual:    [0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1]
>> F1-score: 0.285714
>> Accuracy score: 0.782609
>> C:\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:958:  
   UndefinedMetricWarning:
   F-score is ill-defined and being set to 0.0 due to no predicted samples.

我当然知道问题与我的数据集有关:它太小了(它只是真实数据集的一个样本).但是，有人可以解释我看到的"UndefinedMetricWarning"警告的含义吗?幕后实际上发生了什么?

I certainly know that the problem is related to my dataset: it is too small (it is only a sample of the real one). However, can anybody explain the meaning of the "UndefinedMetricWarning" warning that I am seeing? What is actually happening behind the curtains?

推荐答案

似乎是一个已知的错误此处已修复，我想您应该尝试更新sklearn.

It seems it is a known bug here which has been fixed, I guess you should try update sklearn.

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