L2 Regularization
L2 Regularization, also known as Ridge Regularization, is a method used in machine learning and statistics to prevent overfitting of models. Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on unseen data.
In L2 Regularization, a penalty term is added to the loss function, which is the function used to measure how well the model’s predictions match the actual outcomes. Specifically, the penalty is proportional to the square of the magnitude of the coefficients (weights) of the model. This means that larger weights contribute more to the penalty, effectively discouraging the model from fitting the training data too closely.
The L2 Regularization term is mathematically expressed as:
Penalty = λ * Σ(w_i²)
where:
λ(lambda) is the regularization parameter that controls the strength of the penalty.w_irepresents each weight in the model.
By adjusting the value of λ, practitioners can find a balance between fitting the training data well and keeping the model simple, which enhances its ability to generalize to new data.
Overall, L2 Regularization helps in improving model performance by reducing the risk of overfitting, leading to more robust predictions.