1.1. accuracy_score() 计算所有样本中分类正确样本所占的比例 语法 ## 语法sklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None) y_true:y的真实值 y_pred:y的预测值 normalize:若为True(默认),返回分类得分,若为False,返回分类正确的样本个数 sample_weight:样本权重 ...
sklearn.metrics.auc(x, y, reorder=False) ——— roc_auc_score 直接根据真实值(必须是二值)、预测值(可以是0/1,也可以是proba值)计算出auc值,中间过程的roc计算省略。 形式: sklearn.metrics.roc_auc_score(y_true, y_score, average='macro', sample_weight=None) average : string, [None, ‘mi...
#示例importnumpy as npfromsklearn.metricsimportaccuracy_score y_pred= [0, 2, 1, 3] y_true= [0, 1, 2, 3]print(accuracy_score(y_true, y_pred))#0.5print(accuracy_score(y_true, y_pred, normalize=False))#2#在具有二元标签指示符的多标签分类案例中print(accuracy_score(np.array([[0, ...
在二进制分类中,此函数等于jaccard_score 函数。 例子: >>> 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 在具有二进制标签指...
导入库:from sklearn.metrics import accuracy_score 参数: y_ture:真实标签,可以是个列表; y_pred:标签的预测值,也可以是个列表; (简单的计算准确率到这就可以完成了) Normalize:如果设置为False,则只返回正确预测的个数。否则返回正确预测的比例。默认为True; ...
当normalize为True时,最好的表现是score为1,当normalize为False时,最好的表现是score未样本数量. #示例 import numpy as np from sklearn.metrics import accuracy_score y_pred = [0, 2, 1, 3] y_true = [0, 1, 2, 3] print(accuracy_score(y_true, y_pred)) # 0.5 ...
下面是一些常见的评估指标和它们在sklearn.metrics中的使用方式: 1.分类指标: o准确率(Accuracy):accuracy_score(y_true, y_pred) o精确率(Precision):precision_score(y_true, y_pred) o召回率(Recall):recall_score(y_true, y_pred) oF1分数(F1 Score):f1_score(y_true, y_pred) o混淆矩阵(...
一、sklearn.metrics模块概述 sklearn.metrics是scikit-learn库中用于评估机器学习模型性能的模块。它提供了多种评估指标,如准确率、精确率、召回率、F1分数、混淆矩阵等。这些指标可以帮助我们了解模型的性能,以便进行模型选择和调优。 二、accuracy_score()函数 ...
sklearn.metrics.accuracy_score(y_true,y_pred,*,normalize=True,sample_weight=None) 可用来计算分类准确率分数。 可用来计算多分类准确率分数。 """Accuracy classification score.In multilabel classification, this function computes subset accuracy:the set of labels predicted for a sample must *exactly* ...
from sklearn.metrics import accuracy_score yp = [1, 0, 1, 1] y = [1, 0, 0, 1] print("【显示】yp =",yp) print("【显示】y =",y) print("【执行】accuracy_score(yp, y)") print(accuracy_score(yp, y)) print("【执行】accuracy_score(y, yp)") ...