What is regularization (L1 and L2)?

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Regularization is a technique used in machine learning to prevent overfitting by adding a penalty to the model’s loss function. It discourages the model from learning overly complex patterns that don't generalize well to unseen data.

🔧 Why Regularization?

When a model fits the training data too closely, it may perform poorly on new data. Regularization controls this by penalizing large weights, encouraging simpler models.

📐 Types of Regularization:

🧮 1. L1 Regularization (Lasso):

• Adds the absolute value of coefficients to the loss function:

  $$Loss = Original\ Loss + \lambda \sum |w_i|$$

• Encourages sparsity: drives some weights to zero, effectively performing feature selection.

• Useful when you suspect that only a few features are truly important.

🧮 2. L2 Regularization (Ridge):

• Adds the squared value of coefficients:

  $$Loss = Original\ Loss + \lambda \sum w_i^2$$

• Shrinks all weights toward zero but doesn’t make them exactly zero.

• Helps reduce model complexity while keeping all features.

🧠 λ (Lambda):

• A hyperparameter that controls the strength of regularization.

• Higher λ → stronger penalty → simpler model.

In summary, regularization helps models generalize better by preventing overfitting. L1 encourages sparsity, while L2 ensures smaller, smoother weights.

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