精确召回 pos_label python 为 one-class

Question

目标：获得precision和recall for one-class(y_true = 1)

背景：我检查了http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve，它指出pos_label是positive class的标签，并设置为1 默认。

问题：

1) 如果我只想要 positive class 的 precision 和 recall（在本例中为 y_true = 1），我应该保留 pos_label = 1 还是应该改成 pos_label = 0？

2) 还是有更好的方法来实现我的目标？

下面我显示的是 pos_label = 0

时的代码

import numpy as np
from sklearn.metrics import precision_recall_fscore_support
y_true = np.array(['0', '1', '1', '0', '1'])
y_pred = np.array(['1', '0', '1', '0', '1'])
out = precision_recall_fscore_support(y_true, y_pred, average='weighted', pos_label = 0)

Answer 1

import numpy as np
from sklearn.metrics import precision_recall_fscore_support
y_true = np.array(['0', '1', '1', '0', '1'])
y_pred = np.array(['1', '0', '1', '0', '1'])

#keep 1's
y_true, y_pred = zip(*[[ytrue[i], ypred[i]] for i in range(len(ytrue)) if ytrue[i]=="1"])

out = precision_recall_fscore_support(y_true, y_pred, average='micro')

精确召回 pos_label python 为 one-class

precision recall pos_label python for one-class

python

machine-learning

scikit-learn

precision-recall