【ROC+AUC】 - Code World

【ROC+AUC】

Others 2019-11-29 10:05:22 views: null

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Origin www.cnblogs.com/colipso/p/11955807.html

【ROC+AUC】

Understanding ROC / AUC

Statistics AUC and ROC curve

AUC和ROC曲线

ROC curve and AUC value

Introduction to ROC and AUC and how to calculate AUC

[AI Basics] AUC/ROC Indicator

Machine Learning Basics: AUC and ROC

AUC, ROC I see the most thorough explanation

ROC AUC principle and under sklearn Detailed

[Machine Learning] Understanding AUC-ROC Curve

ROC and AUC calculation, sklearn.metrics roc_curve, precision_recall_curve, auc, roc_auc_score, multi-label classification ROC curve drawing

Почему кривую ROC можно использовать для точной оценки производительности модели, взаимосвязи между auc и roc, когда положительные и отрицательные пробелы в образцах относительно велики, и почему auc может судить, модель хорошая или плохая

ROC classification confusion matrix and assessment indicators of evaluation index] [/ AUC curve

Common evaluation indicators for machine learning: ACC, AUC, ROC curve

Class imbalance (sample imbalance data) processing notes + ROC/AUC curve

Why roc_auc produces weird results in sklearn?

Accuracy, precision, recall, true rate, false positive rate, ROC/AUC

Значения кривой AUC ROC

【ROC + AUC】

【ROC + AUC】

Статистика AUC и кривой ROC

Why the ROC curve can be used to accurately evaluate the model performance, the relationship between auc and roc when the positive and negative sample gaps are relatively large, and why, auc can judge the model is good or bad

Confusion matrix [] classification code template evaluation index of the ROC / AUC code template

5.2 Evaluation indicators of the classifier—accuracy, precision, recall, F1, Fβ, AUC and ROC

sklearn.metrics中的roc_auc_score理解以及使用

Detailed explanation of PR curve, AP, mAP, AUC, ROC curve under Object Detection

错误：ValueError: Only one class present in y_true. ROC_AUC score is not defined in that case

Sklearn.metrics evaluation method described in (accuracy_score, recall_score, roc_curve, roc_auc_score, confusion_matrix, classification_report)

Recall (Recall), precision (Precision), sensitivity (Sensitivity), specificity (Specificity), F1, PR curve, ROC, AUC application scenarios

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