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The Moore–Penrose pseudoinverse generalizes matrix inversion to rectangular or singular matrices and is denoted A⁺.
Elastic Net regularization combines L1 (Lasso) and L2 (Ridge) penalties to produce models that are both sparse and stable.
L2 regularization (also called ridge or weight decay) adds a penalty proportional to the sum of squared weights to discourage large parameters.