This regularization penalizes weights proportionally to their squares. L2 regularization brings high-positive and low-negative outlier weights closer to 0 but not quite to 0. Features with values near 0 remain in the model but don’t affect the prediction. L2 regularization always improves linear model generalization.
Another method used in AI and machine learning to prevent overfitting and improve model generalization is L2 regularization, also known as Ridge regularization. L2 regularization adds a penalty term to the loss function during training proportional to the square of the model’s weights, unlike L1 regularization, which encourages sparse solutions.
Calculating the L2 regularization penalty:
L2 = λ * ∑ (wi)^2
Where
L2 – L2 regularization penalty term,
λ – a hyperparameter that controls the penalty strength
wi – the model weights
∑ – summation of overall weights.
L2 regularization reduces weights without setting any to zero. This reduces model complexity and improves generalization.
Applications of L2 Regularization
Linear regression uses L2 regularization by adding the L2 penalty term to the cost function during training and minimizing it with an optimization algorithm like gradient descent. Like linear regression, logistic regression and neural networks use L2 regularization by adding the L2 penalty term to the loss function during training.
Image processing, NLP, and recommendation systems use L2 regularization. It prevents overfitting and improves machine learning model generalization in AI. L2 regularization can simplify and improve unseen data performance by adding a penalty term to the loss function during training to encourage smaller weights.
