一、分类评估指标
from sklearn.metrics import accuracy_score y_pred = [0, 2, 1, 3] y_true = [0, 1, 2, 3] accuracy_score(y_true, y_pred) 0.5 accuracy_score(y_true, y_pred, normalize=False) 2
from sklearn.metrics import balanced_accuracy_score y_true = [0, 1, 0, 0, 1, 0] y_pred = [0, 1, 0, 0, 0, 1] balanced_accuracy_score(y_true, y_pred) 0.625
from sklearn.metrics import confusion_matrix
y_true = [2, 0, 2, 2, 0, 1] y_pred = [0, 0, 2, 2, 0, 2] confusion_matrix(y_true, y_pred) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]])
可以推导出混淆矩阵如下所示
Example2:多分类任务，类别没有转换为整型数值，而是作为字符串直接传入
from sklearn.metrics import confusion_matrix
y_true = ["cat", "ant", "cat", "cat", "ant", "bird"] y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"] confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"]) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]])
证明该API可以直接传入类别型字符串，混淆矩阵如下所示
Example3:二分类任务
from sklearn.metrics import confusion_matrix confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]) array([[0, 2], [1, 1]], dtype=int64) tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel() (tn, fp, fn, tp) (0, 2, 1, 1)
此处“1”为正例，“0”为反例
Roc曲线及auc值
适用范围：二分类
介绍：roc曲线为二维平面的一条曲线，横坐标为fpr，纵坐标为tpr，auc（area under the curve）顾名思义，即为曲线与横轴围成的曲线下的面积
API：
sklearn中有三个关于roc计算相关的接口，分别为
1 metrics.roc_auc_score() 2 metrics.roc_curve() 3 metrics.auc()
1）直接计算auc的值
from sklearn import metrics import numpy as np
y_true = np.array([0, 0, 1, 1]) y_scores = np.array([0.1, 0.4, 0.35, 0.8]) metrics.roc_auc_score(y_true, y_scores) 0.75
2）先计算fpr,tpr以及threshold（阈值）,再通过fpr和tpr求auc
from sklearn import metrics import numpy as np y_true = np.array([0, 0, 1, 1]) y_scores = np.array([0.1, 0.4, 0.35, 0.8]) fpr, tpr, threshold = metrics.roc_curve(y_true, y_scores) (fpr, tpr, threshold) (array([0. , 0.5, 0.5, 1. ]), array([0.5, 0.5, 1. , 1. ]), array([0.8 , 0.4 , 0.35, 0.1 ])) metrics.auc(fpr, tpr) 0.75
注意接口接收的参数为类别的真实标签以及模型输出的概率（得分）值，而非模型预测的标签（标签需要通过概率+阈值来得到）
1 metrics.precision_recall_curve() 2 metrics.precision_recall_fscore_support() 3 metrics.precision_score() 4 metrics.recall_score() 5 metrics.fbeta_score()
1)计算pr曲线
接口接收两个参数，其一为样本的真实值，另一个为样本的预测概率（得分），示例如下：
import numpy as np from sklearn.metrics import precision_recall_curve y_true = np.array([0, 0, 1, 1]) y_scores = np.array([0.1, 0.4, 0.35, 0.8]) precision, recall, thresholds = precision_recall_curve(y_true, y_scores) precision, recall, thresholds (array([0.66666667, 0.5 , 1. , 1. ]), array([1. , 0.5, 0.5, 0. ]), array([0.35, 0.4 , 0.8 ]))
2)计算当预测值已经通过阈值转化为类别后的具体某一阈值下的PR及F-score值
from sklearn import metrics #该函数可直接求出precision、recall和f-score
precision,recall,fbeta_score,support = metrics.precision_recall_fscore_support()
#以下三个函数可分别求出precision、recall和f-score
precision = metrics.precision_score()
recall = metrics.recall_score()
f-score = metrics.fbeta_score()
1 metrics.cohen_kappa_score() 2 metrics.hamming_loss() 3 metrics.hinge_loss() 4 metrics.jaccard_score() 5 metrics.log_loss()
from sklearn.metrics import matthews_corrcoef y_true = [+1, +1, +1, -1] y_pred = [+1, -1, +1, +1] matthews_corrcoef(y_true, y_pred) -0.33333333
二、回归评估指标
基于sklearn的metrics库的常用有监督模型评估指标学习
原文：https://www.cnblogs.com/tianyadream/p/12348171.html