Regularization is a technique used to prevent overfitting in machine learning models. It adds a penalty term to the loss function during training, discouraging the model from assigning excessive importance to certain features. Regularization helps to simplify the model by reducing the complexity and variance, thereby improving its generalization performance on unseen data.
Example code:
# Example code illustrating regularization in linear regression
from sklearn.linear_model import Ridge
ridge_model = Ridge(alpha=0.5) # alpha is the regularization strength
ridge_model.fit(X_train, y_train)
ridge_predictions = ridge_model.predict(X_test)
ridge_mse = mean_squared_error(y_test, ridge_predictions)
