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Beyond Accuracy: The Complete Evaluation Toolkit for Logistic Classifiers
정확도의 거짓말: 로지스틱 회귀 모델을 제대로 평가하는 법
Why it matters
Most developers rely solely on accuracy to evaluate classifiers, only to discover in production that high-accuracy models fail catastrophically. This guide reveals why single-metric evaluation is dangerously incomplete and teaches the comprehensive toolkit—confusion matrix, precision, recall, F1, ROC/AUC—that separates illusions from production-ready models.
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logistic regressionbinary classificationconfusion matrixROC/AUCprecision recallF1 score