Remedies for Class Imbalance

54 min
Block 13 — Class imbalance
Objective
turn the diagnosis of chapter 050 into a procedure ordered by cost and risk; derive the Bayes-optimal threshold and establish by measurement that moving it is the first remedy and usually the sufficient one; show that class weighting is a threshold move plus a rotation of the boundary; define random oversampling, random undersampling, SMOTE and its variants, the informed undersamplers and the combined methods precisely enough to reproduce them, and measure what each is worth; establish the rule that resampling belongs inside the cross-validation fold and quantify the fiction produced by breaking it; report honestly where synthetic oversampling helps and where it hurts; frame the subject as expected-cost minimization; cover the imbalance-aware ensembles, the one-class framing, the return on collecting positives, and the calibration damage every remedy causes; and close with a decision table.
Estimated duration
95 minutes
Prerequisites
chapter 050 in full; chapters 026, 027, 028, 034 and 035 for the protocol; chapter 049 for the model families; chapter 030 for the distinction between a loss and a metric
Associated quizzes
051.1-quiz-the-remedy-ladder.md to 051.8-quiz-decision-procedure.md

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This lesson is part of the “Imbalanced Data and Resampling” module

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