包含:precision/recall/fi-score/均值/分类个数 . 6、 kappa score kappa score是一个介于(-1, 1)之间的数. score>0.8意味着好的分类;0或更低意味着不好(实际是随机标签) 代码语言:javascript 复制 from sklearn.metricsimportcohen_kappa_score y_true=[2,0,2,2,0,1]y_pred=[0,0,2,2,0,2]cohen...
1、sklearn.metrics.recall_score()方法 方法说明: sklearn.metrics.recall_score(y_true, y_pred, *, labels=None, pos_label=1,average='binary', sample_weight=None,zero_division="warn"): 1. 参数介绍: y_true:真实的标签,即数据集中真实的分类标签情况,是一个1维的数组 y_pred:预测标签,即模型...
sklearn.metrics.auc(x, y) 1. 参数: x:fpr y:tpr 首先要通过roc_curve计算出fpr和tpr的值,然后再metrics.auc(fpr, tpr) 返回:auc的值 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) 其中Pn和Rn是第n个阈值处的precision和recall。对于...
#导入相应的函数库fromsklearn.metricsimportaccuracy_scorefromsklearn.metricsimportprecision_scorefromsklearn.metricsimportconfusion_matrixfromsklearn.metricsimportclassification_reportfromsklearn.metricsimportcohen_kappa_scorefromsklearn.metricsimportf1_scorefromsklearn.ensembleimportRandomForestClassifierfromsklearnimpo...
python sklearn计算准确率、精确率、召回率、F1 score https://blog.csdn.net/hfutdog/article/details/88085878 混淆矩阵 准确率 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 ...
recall_score:召回 f1_score:f1值 roc_curve:roc曲线 5、代码实例 例1: >>>importnumpy as np>>>fromsklearn.imputeimportSimpleImputer>>> imp = SimpleImputer(missing_values=np.nan, strategy='mean')>>> imp.fit([[1, 2], [np.nan, 3], [7, 6]]) ...
from sklearn.metrics import recall_score recall_score(y_test, y_log_predict) # Out[17]: # 0.80000000000000004 有时候我们注重精准率,如股票预测 预测股票上涨的情况下,实际股票是涨的概率越大越好;而对于前提是股票已经上涨的召回率来看,它的大小不是那么重要。
from sklearn.metrics import precision_score, recall_score from sklearn.datasets import load_breast_cancer # 加载乳腺癌数据集 data = load_breast_cancer() X, y = data.data, data.target # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, ...
python中想要计算如上指标主要是使用sklearn包中预先写好的函数。可以使用以下代码进行计算: fromsklearn.metricsimportprecision_score, recall_score, f1_score, accuracy_scorey_true = [...]# 正确的标签y_pred = [...]# 预测的标签# 计算正确率accuracy = accuracy_score(y_true, y_pred)# 计算精确度...
("Precision",sk.metrics.precision_score(y_true,y_pred))print("Recall",sk.metrics.recall_score(y_true,y_pred))print("f1_score",sk.metrics.f1_score(y_true,y_pred))print("confusion_matrix")print(sk.metrics.confusion_matrix(y_true,y_pred))fpr,tpr,tresholds=sk.metrics.roc_curve(y_...