用于多类分类的sklearn指标 [英] sklearn metrics for multiclass classification

问题描述

我已经使用sklearn执行了GaussianNB分类.我尝试使用以下代码来计算指标:

I have performed GaussianNB classification using sklearn. I tried to calculate the metrics using the following code:

print accuracy_score(y_test, y_pred)
print precision_score(y_test, y_pred)

准确度分数正常运行，但精确度分数计算显示错误为:

Accuracy score is working correctly but precision score calculation is showing error as:

ValueError:目标是多类的，但average ='binary'.请选择其他平均设置.

ValueError: Target is multiclass but average='binary'. Please choose another average setting.

由于目标是多类的，我可以得到精度，召回率等度量指标吗?

As target is multiclass, can i have the metric scores of precision, recall etc.?

推荐答案

函数调用 precision_score(y_test，y_pred)等效于 precision_score(y_test，y_pred，pos_label = 1，平均值='binary').该文档( http://scikit-learn.org/stable/modules/generation/sklearn.metrics.precision_score.html )告诉我们:

The function call precision_score(y_test, y_pred) is equivalent to precision_score(y_test, y_pred, pos_label=1, average='binary'). The documentation (http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) tells us:

二进制":

仅报告由pos_label指定的类的结果.仅在目标(y_ {true，pred})是二进制的情况下适用.

Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred}) are binary.

所以问题是您的标签不是二进制的，而是可能是一键编码的.幸运的是，还有其他选项可以处理您的数据:

So the problem is that your labels are not binary, but probably one-hot encoded. Fortunately, there are other options which should work with your data:

precision_score(y_test，y_pred，average = None)将返回每个类的精度得分，而

precision_score(y_test, y_pred, average=None) will return the precision scores for each class, while

precision_score(y_test，y_pred，average ='micro')将返回总比率tp/(tp + fp)

precision_score(y_test, y_pred, average='micro') will return the total ratio of tp/(tp + fp)

如果您选择了 binary 以外的其他 average 选项，pos_label 参数将被忽略.

The pos_label argument will be ignored if you choose another average option than binary.

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