Top Posts Tagged with #auroc

AUC ROC Analysis Explained: A Powerful Guide to Reliable Machine Learning Evaluation

AUC-ROC analysis is a crucial metric for evaluating classification models by measuring their ability to distinguish between classes. It helps compare model performance across different thresholds, ensuring reliable and unbiased evaluation. Mastering AUC-ROC leads to more accurate and trustworthy machine learning Read More..

#AUROC #MachineLearningEvaluation #DataScience #ModelPerformance #MLMetrics

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모델을 학습시켰는데 "좋다"는 게 뭔가요?정확도 99%가 항상 최고일까요? 암 진단 AI가 정확도 99%라며 자랑하는데, 실제 암 환자 100명 중 90명을 놓친다면? 같은 모델인데 평가 지표만 바꿨더니 최고에서 최악으로 추락하는 충격적인 현실! MSE, RMSE, MAE부터 AUROC, F1-Score, mAP, BLEU, ROUGE, Perplexity, IoU, FID, CLIPScore, BERTScore까지, 데이터 타입별·Task별·응용 분야별로 어떤 지표를 써야 하는지 완벽 정리했습니다. #Accuracy #AI평가지표 #AUROC #CrossEntropy #DiceLoss #F1Score #PolicyGradient #REINFORCE #sigmoidF1 #SurrogateLoss #강화학습 #머신러닝평가 #미분가능 #손실함수 Read the full article

#Accuracy #AI평가지표 #AUROC #Cross-Entropy #DiceLoss #F1-Score #PolicyGradient #REINFORCE #sigmoidF1 #SurrogateLoss #강화학습 #머신러닝평가 #미분가능 #손실함수