L1 and L2 points, also known as L1 and L2 norms, are mathematical measures used in machine learning and statistics to quantify the complexity or "size" of a model. They represent the sum of the absolute values (L1) or squared values (L2) of the model's coefficients.
L1 and L2 points act as regularization parameters, which help prevent overfitting in a model by penalizing large coefficients. This leads to a simpler and more generalizable model that performs better on unseen data.
The main difference between L1 and L2 points is the way they penalize large coefficients. L1 points tend to shrink the less important coefficients to zero, effectively performing feature selection, while L2 points shrink all coefficients towards zero but rarely make them exactly zero.
Yes, L1 and L2 points can be used together in a technique called elastic net regularization. This combines the strengths of both L1 and L2 points, allowing for feature selection and coefficient shrinkage, while also handling collinearity among features.
The choice between L1 and L2 points depends on the specific dataset and modeling goals. L1 points may be preferred for feature selection, while L2 points may be more suitable for handling collinearity. It is recommended to try both and compare the performance of the models to determine the best approach for a particular problem.