To prevent overfitting, Lasso provides a “penalty” for mannequin complexity, controlled by a parameter (λ) that adjusts the trade-off between simplicity and accuracy. The SVM would discover the optimum boundary between these two classes within the high-dimensional function house, allowing for correct classification of recent, unseen instances. The algorithm focuses on the “assist vectors” –

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