【分类指标】
1.accuracy_score(y_true,y_pre) : 精度
2.auc(x, y, reorder=False) : ROC曲线下的面积;较大的AUC代表了较好的performance。
3.average_precision_score(y_true, y_score, average=‘macro‘, sample_weight=None):根据预测得分计算平均精度(AP)
4.brier_score_loss(y_true, y_prob, sample_weight=None, pos_label=None):The smaller the Brier score, the better.
5.confusion_matrix(y_true, y_pred, labels=None, sample_weight=None):通过计算混淆矩阵来评估分类的准确性 返回混淆矩阵
6.f1_score(y_true, y_pred, labels=None, pos_label=1, average=‘binary‘, sample_weight=None): F1值
F1 = 2 * (precision * recall) / (precision + recall) precision(查准率)=TP/(TP+FP) recall(查全率)=TP/(TP+FN)
7.log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None, labels=None)：对数损耗，又称逻辑损耗或交叉熵损耗
8.precision_score(y_true, y_pred, labels=None, pos_label=1, average=‘binary‘,) ：查准率或者精度； precision(查准率)=TP/(TP+FP)
9.recall_score(y_true, y_pred, labels=None, pos_label=1, average=‘binary‘, sample_weight=None)：查全率 ；recall(查全率)=TP/(TP+FN)
10.roc_auc_score(y_true, y_score, average=‘macro‘, sample_weight=None)：计算ROC曲线下的面积就是AUC的值，the larger the better
11.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)；计算ROC曲线的横纵坐标值，TPR，FPR
TPR = TP/(TP+FN) = recall(真正例率，敏感度) FPR = FP/(FP+TN)(假正例率，1-特异性)
【回归指标】
1.explained_variance_score(y_true, y_pred, sample_weight=None, multioutput=‘uniform_average‘)：回归方差(反应自变量与因变量之间的相关程度)
2.mean_absolute_error(y_true, y_pred, sample_weight=None, multioutput=‘uniform_average‘)：平均绝对误差
3.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput=‘uniform_average‘)：均方差
4.median_absolute_error(y_true, y_pred) 中值绝对误差
5.r2_score(y_true, y_pred, sample_weight=None, multioutput=‘uniform_average‘) ：R平方值
原文：https://www.cnblogs.com/vmkash/p/14593740.html