sklearn.metrics.roc_auc_score(y_true, y_score, *, average='macro', sample_weight=None, max_fpr=None, multi_class='raise', labels=None) y_true:y的真实标签 y_score:估计器计算出的每个样本属于每种类别的概率,如果是二分类,则是estimator.predict_proba(X)[:,1],或者是estimator.decision_funct...
sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None) normalize:默认值为True,返回正确分类的比例;如果为False,返回正确分类的样本数 ——— recall_score 召回率 =提取出的正确信息条数 /样本中的信息条数。通俗地说,就是所有准确的条目有多少被检索出来了。 形式: klearn.m...
#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, ...
print(metrics.precision_score(y_true, y_pred)) #precision_score仅支持二元分类,及0,1分类 print(metrics.recall_score(y_true, y_pred)) print(metrics.fbeta_score(y_true, y_pred,beta=1)) #f参数需要指定β的大小 print(metrics.precision_recall_fscore_support(y_true, y_pred,beta=1)) 1. ...
print(accuracy_score(y_true,y_pred,normalize=True,sample_weight=None)) 结果为0.25 计算召回率 导入库:from sklearn.metrics import recall_score 参数: y_true:真实标签; y_pred:预测标签; labels:当average!=binary时,要计算召回率的标签集合,是个列表,默认None。要计算标签为1和2的召回率时labels=[1,...
>>> 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示符的多标签情况下: >>> import numpy as np >>> ...
SKlearn的Metrics模块下有有许多二分类算法的评价指标,这里我们主要讨论最常用的几种。 ML evaluation.png 1.准确度(Accuracy) fromsklearn.metricsimportaccuracy_score(y_true,y_pred,normalize=True,sample_weight=None) 1.1参数说明 y_true:数据的真实label值 ...
有三种方法可以为scoring参数指定多个评分指标: 字符串度量指标构成的列表: scoring = ['accuracy', 'precision'] score名称和score函数构成的字典 from sklearn.metrics import accuracy_score from sklearn.metrics import make_scorer scoring = {'accuracy': make_scorer(accuracy_score), ...
from sklearn import metrics 1.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None) 参数分别为y实际类别、预测类别、返回值要求(True返回正确的样本占比,false返回的是正确分类的样本数量) eg: >>> import numpy as np >>> from sklearn.metrics import accuracy_score ...