Metrics.roc_Auc_Score. ‘roc_auc’ metrics.roc_auc_score ‘roc_auc_ovr’ metrics.roc_auc_score ‘roc_auc_ovo’ metrics.roc_auc_score ‘roc_auc_ovr_weighted’ metrics.roc_auc_score.web Sklearn.metrics.roc_auc_score¶ , , ,sklearn.metrics.roc_auc_score (y_true, y_score, average='macro', sample_weight=none) [source] ¶ , compute area under the curve (auc) from.web
sklearn：auc、roc_curve、roc_auc_scoreCSDN博客
It all depends on how you got the input for the auc () function. Compute area under the curve (auc) using the trapezoidal rule. Roc_auc_score (y_true, y_score, *, average = 'macro', sample_weight = none, max_fpr = none, multi_class = 'raise', labels = none) [source] ¶ compute area under the receiver operating.web Metrics.auc (fpr, tpr), and then it's natural that auc () and roc_auc_score.web ‘roc_auc’ metrics.roc_auc_score ‘roc_auc_ovr’ metrics.roc_auc_score ‘roc_auc_ovo’ metrics.roc_auc_score ‘roc_auc_ovr_weighted’ metrics.roc_auc_score.web Say, sklearn suggests fpr, tpr, thresholds = metrics.roc_curve (y, pred, pos_label=2); This is a general function, given points on a curve. Sklearn.metrics.roc_auc_score¶ , , ,sklearn.metrics.roc_auc_score (y_true, y_score, average='macro', sample_weight=none) [source] ¶ , compute area under the curve (auc) from.web
Sklearn.metrics.roc_auc_score¶ , , ,sklearn.metrics.roc_auc_score (y_true, y_score, average='macro', sample_weight=none) [source] ¶ , compute area under the curve (auc) from.web Metrics.auc (fpr, tpr), and then it's natural that auc () and roc_auc_score.web ‘roc_auc’ metrics.roc_auc_score ‘roc_auc_ovr’ metrics.roc_auc_score ‘roc_auc_ovo’ metrics.roc_auc_score ‘roc_auc_ovr_weighted’ metrics.roc_auc_score.web Roc_auc_score (y_true, y_score, *, average = 'macro', sample_weight = none, max_fpr = none, multi_class = 'raise', labels = none) [source] ¶ compute area under the receiver operating.web Say, sklearn suggests fpr, tpr, thresholds = metrics.roc_curve (y, pred, pos_label=2); Sklearn.metrics.roc_auc_score¶ , , ,sklearn.metrics.roc_auc_score (y_true, y_score, average='macro', sample_weight=none) [source] ¶ , compute area under the curve (auc) from.web It all depends on how you got the input for the auc () function. Compute area under the curve (auc) using the trapezoidal rule. This is a general function, given points on a curve.
