Mastering Model Complexity: A Deep Dive into Regularization Techniques

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In the ever-evolving landscape of machine learning, ensuring that models generalize well to unseen data is a paramount challenge. Regularization techniques play a crucial role in addressing the specter of overfitting, where models become too complex and fail to generalize beyond the training data. Here’s a list of common regularization techniques:

I. L1 Regularization (Lasso):

• L1 regularization, also known as Lasso, introduces absolute values of coefficients as a penalty term.
• By driving some coefficients to zero, it encourages sparsity in the model, effectively selecting only the most important features.

# Apply L1 regularization (Lasso)
alpha = 0.01  # Regularization strength
lasso_model = Lasso(alpha=alpha)
lasso_model.fit(X_train, y_train)

II. L2 Regularization (Ridge):

• In contrast, L2 regularization (Ridge) adds the squared values of coefficients as a penalty term. This encourages small weights without forcing them to be precisely zero, promoting a more balanced complexity.
• Encourages small weights but doesn’t force them to be exactly zero.

# Apply L2 regularization (Ridge)
alpha = 0.01  # Regularization…