23 - Keras Multiclass Classification for Deep Neural Networks with ROC and AUC ( 4 0 2023-12-05 17:38:11 您当前的浏览器不支持 HTML5 播放器 请更换浏览器再试试哦~点赞 投币 收藏 分享https://www.youtube.com/watch?v=rdRhtbMrWYg&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN 科技 计算机技...
py in roc_auc_score(y_true, y_score, average, sample_weight, max_fpr, multi_class, labels) 564 if multi_class == "raise": 565 raise ValueError("multi_class must be in ('ovo', 'ovr')") --> 566 return _multiclass_roc_auc_score( 567 y_true, y_score, labels, multi_class, ...
roc_auc_scorefromsklearn.datasetsimportload_irisfromsklearn.multiclassimportOneVsRestClassifierfromsklearn.model_selectionimporttrain_test_splitimportmatplotlib.pyplotasplt# Load the iris datasetiris=load_iris()# Split the dataset into training and test setsX_train,X_test,y_train,y_test=train_test_...
metrics.precision_score(y_true, y_pred, average='micro') # 微平均,精确率 Out[130]: 0.33333333333333331 metrics.precision_score(y_true, y_pred, average='macro') # 宏平均,精确率 Out[131]: 0.375 metrics.precision_score(y_true, y_pred, labels=[0, 1, 2, 3], average='macro') # 指定...
average— Compute performance metrics for an average ROC curve for multiclass problems. addMetrics— Compute additional classification performance metrics. auc— Compute the area under the ROC curve or precision-recall curve. You can also compute the confidence intervals of performance curves by providin...
查准率，表示所有被预测为正类的样本（TP+FP）是真正类（TP）的比例： P = T P T P + F P...
其中,当α = 1时,则 F-Score 即为F1: 当有多个类别时,我们对各个类别的F1-Score求均值,就是最后的F1-score 4、P-R Curve(精确率-召回率 曲线) 在P-R曲线中,横坐标是recall,纵坐标是precision。下图就是一个P-R曲线的例子: 5、ROC曲线,AUC面积(FPR - FPR 曲线) ...
rocObj is a rocmetrics object that stores the performance metrics for each class in the Metrics property. Compute the AUC values using the auc function. Get a = auc(rocObj) a = 1×2 0.8587 0.8587 For a binary classification problem, the AUC values are equal to each other. The tabl...
micro F1对每类TP,FP先加起来，再求F1。图片来源：Plant Seedlings Classification | Kagglewww....
AUC 很適合用來比較兩個不同模型 但前提是資料集要大致達到平衡(請參閱精確度與喚回度曲線, 以達到不平衡的資料集)。中面積較大的模型 通常代表曲線較佳。 圖3.包含兩個假想模型的 ROC 和 AUC。圖表上的 而 AUC 越多代表這兩個模型越好 ROC 曲線上最接近 (0,1) 的點表示 指定模型表現最佳門檻。如門檻...