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Machine Learning Classification: Metrics, Thresholds, Error Analysis, and Production Design
A practical guide to machine learning classification covering MNIST, confusion matrices, precision, recall, F1, threshold selection, ROC-AUC, imbalanced data, multiclass and multilabel classification, error analysis, and production ML system design.
Deepak Mishra26 min read
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