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Revati S Misra

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Aryan Kumar

Aryan Kumar

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:

  1. L1 Regularization (Lasso): L1 regularization adds the absolute values of the model's parameter values to the objective function. It encourages sparsity in the model by driving some of the parameter values to zero. This can help in feature selection and feature elimination, as it effectively performs automatic feature selection.
  2. L2 Regularization (Ridge): L2 regularization adds the squared values of the model's parameter values to the objective function. It penalizes large parameter values, leading to a smoother model and reducing the impact of individual features. L2 regularization is often used to prevent overfitting and improve the stability of the model.
  3. Elastic Net Regularization: Elastic Net combines L1 and L2 regularization by adding both penalties to the objective function. It provides a balance between feature selection (L1) and parameter shrinkage (L2), and it is useful when there are correlated features in the data.

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.

 

 


 

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