Regularization in Machine Learning

Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the model's loss function. This penalty term helps to control the complexity of the model and improve its generalization performance on unseen data.

Techniques for Regularization in Machine Learning

L1 Regularization (Lasso)

L1 regularization adds the absolute values of the coefficients as a penalty term to the loss function. This technique helps to enforce sparsity in the model by pushing some coefficients to zero.

L2 Regularization (Ridge)

L2 regularization adds the squared magnitudes of the coefficients as a penalty term. It is effective at reducing the impact of outliers in the data and helping to handle multicollinearity.

Elastic Net Regularization

Elastic Net regularization combines L1 and L2 regularization by adding both penalty terms to the loss function. This technique provides a balance between feature selection (L1) and handling correlated features (L2).

Dropout Regularization

Dropout is a technique commonly used in neural networks to randomly drop some neurons during training, forcing the network to learn more robust features and reducing overfitting.

By understanding and utilizing different regularization techniques in machine learning, one can build more reliable and generalizable models that perform well on unseen data.