◑ applying · kind concept · level 3 · 20h
- Aplica: Linear Regression
Penalising model complexity to curb overfitting: L2 (ridge) shrinks weights, L1 (lasso) drives them to zero for selection, ElasticNet blends both.
In mathematics, Statistics, finance, and computer science, particularly in machine learning and inverse problems, regularization is a process that converts the answer to a problem to a simpler one. It is often used in solving ill-posed problems or to prevent overfitting. There is a strong connection between regularization methods and Bayesian approaches for solving such ill-posed problems.
Enlaces
- Aplica: Linear Regression