L1 regularization, also known as Lasso regularization, is a technique used in machine learning to add a penalty term to the loss function based on the absolute values of the model’s weights. The regularization term is proportional to the sum of the absolute values of the weights, encouraging sparsity in the model. Here, I’ll discuss the impact of L1 regularization on error metrics, focusing on its influence on model sparsity and the resulting trade-offs.
I. Impact on Model Sparsity:
1. Feature Selection:
- L1 regularization promotes feature selection by encouraging many feature weights to be exactly zero.
- Features with zero weights are effectively ignored by the model during prediction, leading to a sparse model where only a subset of features is considered.
2. Sparse Models:
- As the strength of L1 regularization increases, more weights tend to become exactly zero.
- The degree of sparsity is controlled by the regularization strength, often denoted by the hyperparameter α or λ.
