What is regularization in ML?

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Regularization in Machine Learning is a technique used to prevent overfitting by adding a penalty term to the model’s loss function. Overfitting happens when a model learns not only the true patterns but also the noise in the training data, leading to poor performance on unseen data.

🔹 How Regularization Works

• The idea is to discourage the model from assigning too much importance (very large weights) to specific features.

• This is done by adding a penalty to the loss function that the model is trying to minimize.

• The penalty grows when model parameters (weights) become too large, pushing the model to be simpler and more generalizable.

🔹 Common Types of Regularization

1. L1 Regularization (Lasso)

   • Adds the absolute value of weights to the loss function.

   • Can shrink some weights to exactly zero, effectively performing feature selection.

2. L2 Regularization (Ridge)

   • Adds the squared value of weights to the loss function.

   • Tends to make weights smaller but rarely zero, distributing importance across features.

3. Elastic Net

   • Combines L1 and L2 penalties.

   • Useful when features are correlated, balancing both sparsity (L1) and stability (L2).

🔹 Why It’s Important

• Prevents overfitting → Improves generalization on test data.

• Simplifies models → L1 can remove irrelevant features.

• Stabilizes learning → Helps gradient-based methods converge more reliably.

✅ In short: Regularization is a way to control model complexity by penalizing large weights, helping prevent overfitting and improving the model’s ability to generalize.

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