A regularization that penalizes weights by their sum of absolute values. L1 regularization reduces irrelevant feature weights to 0. Zero-weighted features are eliminated from the model.
L1 regularization, commonly known as Lasso regularization, prevents overfitting and improves generalization in AI machine learning models like linear regression, logistic regression, and neural networks.
Mathematical representation of L1 regularization.
L1 = λ * ∑ |wi|
where
L1 – L1 regularization penalty term
λ – a hyperparameter controls the penalty strength
wi – the model weights
∑ – the summation done for the overall weights.
L1 regularization pushes weights towards zero, simplifying the model and minimal features. This can help with feature selection and model simplification, improving the generalization on unknown and unseen data.
Applications of L1 Regularization
Linear regression
Linear regression uses L1 regularization by adding the L1 penalty term to the cost function during training, which is later minimized using an optimization algorithm like gradient descent. The L1 penalty term enables the model to build sparse output, which can be used for feature selection and model simplification, improving generalization performance.
Logistic regression
Similar to linear regression, L1 regularization is applied to logistic regression by adding the L1 penalty term to the loss function during training. This allows the model to generate sparse solutions, improving generalization and preventing overfitting.
