推荐系统-排序算法：常用评价指标：NDCG、MAP、MRR、HR、ILS、ROC、AUC、F1等 - Code World

推荐系统-排序算法：常用评价指标：NDCG、MAP、MRR、HR、ILS、ROC、AUC、F1等

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Origin blog.csdn.net/u013250861/article/details/121071550

推荐系统-排序算法：常用评价指标：NDCG、MAP、MRR、HR、ILS、ROC、AUC、F1等

推荐系统-排序算法：常用评价指标：NDCG、MAP、MRR、HR、ILS、ROC、AUC、F1等

推荐系统-排序算法：常用评价指标：NDCG、MAP、MRR、HR、ILS、ROC、AUC、F1等

Commonly used evaluation indicators for deep learning (Accuracy, Recall, Precision, HR, F1 score, MAP, MRR, NDCG) - recommendation system

评价指标(精准率、召回率、f1、macro f1、micro f1、weighted f1)

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

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

[Summary of model evaluation indicators in deep learning (confusion matrix, recall, precision, F1, AUC area, ROC curve, ErrorRate)]

Classification indicators: accuracy rate, precision rate, recall rate, F1 score and ROC, AUC, macro average, weighted average

机器学习算法评价指标—“查准率”、“查全率”、“F1-score”

Recommended evaluation indicators commonly used in the system: HR, NDCG, MRR

[Recommendation system] common evaluation indicators NDCG, HR, Recall, MRR analysis

【推荐搜索评价指标二】MAP-概念-原理-推导-代码

推荐中的评估指标：AUC、GAUC

top n推荐的数据集划分和评价指标

NDCG, AUC introduction

目标检测中的评价指标mAP以及coco评价标准

深度学习评价指标

Understanding ROC / AUC

【ROC+AUC】

Statistics AUC and ROC curve

AUC和ROC曲线

ROC curve and AUC value

5.2 Показатели оценки классификатора - точность, прецизионность, отзывчивость, F1, Fβ, AUC и ROC.

Precision, Recall, and F1

Precision, Recall, and F1

常见的几种推荐系统算法

Introduction to ROC and AUC and how to calculate AUC

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

机器学习之评价指标(一)——回归评价指标

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