Author(s): Souradip Pal

Originally published on Towards AI.

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The story of regularization starts with a simple yet crucial problem that haunts many machine learning models: overfitting. Picture this — you’ve built a model that fits your training data perfectly, predicting every data point like a seasoned expert. But the moment you feed it unseen data, it starts to falter, making wild predictions that leave you scratching your head.

This is where regularization steps in like a savior. It’s a strategy to simplify your model, reduce overfitting, and ensure it generalizes well. Let’s dive into what regularization really is, the popular methods — Ridge, Lasso, and Elastic Net — and how these tools can help us strike the right balance between fitting and generalizing.

Regularization is a set of techniques used to prevent a model from being too complex, ensuring it doesn’t “over-learn” the training data. In simpler terms, it penalizes extreme parameter values, nudging the model toward simplicity.

At the heart of it, regularization introduces a penalty term to the loss function — the function the model tries to minimize during training. While a typical loss function might aim to reduce the difference between predictions and actual outcomes, regularization adds… Read the full blog for free on Medium.

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Published via Towards AI