Inference, estimation, and decision-making from data
OLS finds the coefficients that fit the training data best, which is exactly the problem when you have many features or little data: it fits the noise too, and the coefficients swing to wild values. Regularized regression tames this by adding a penalty that punishes large coefficients, trading a little training fit for much better generalization.
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▶ Regularized Regression