Module
Overfitting, Bias-Variance, and Regularization
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About
Diagnose overfitting/underfitting via learning curves; bias-variance decomposition intuition; L2/L1 regularization, early stopping, and validation-driven model selection.
Goal
Generate learning curves that show over/underfitting; add Ridge/Lasso (or weight decay) and tune λ on validation; explain the bias-variance tradeoff in plain language and with a simple experiment.
Prerequisites
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