What is regularization in Machine Learning and its purpose?
What is regularization in Machine Learning and its purpose?
Ask by Revati S Misra
Updated 29 May 2023
Regularization is a technique used in machine learning to prevent overfitting and improve the generalization performance of a model. Overfitting occurs when a model learns the training data too well, including noise or irrelevant patterns, and fails to generalize well to unseen data. Regularization helps to address this issue by adding a penalty term to the model's objective function, encouraging simpler models with reduced complexity.
The purpose of regularization is to strike a balance between fitting the training data well and avoiding excessive complexity. By adding a regularization term, the model is discouraged from relying too heavily on specific features or relationships in the training data that may not be representative of the true underlying patterns.
The regularization term is typically a function of the model's parameters, and it is added to the loss function or the objective function during the training process. The most commonly used types of regularization techniques are:
The effect of regularization is controlled by a hyperparameter called the regularization parameter (lambda or alpha), which determines the strength of the regularization penalty. The optimal value of the regularization parameter is typically determined through techniques such as cross-validation.
Regularization helps in preventing overfitting by discouraging complex models that may fit the training data too closely but fail to generalize well. It promotes models that are more robust and have better generalization performance on unseen data. By reducing overfitting, regularization can lead to improved model accuracy and stability, especially in situations with limited training data or high-dimensional feature spaces.