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    Regularization is a crucial technique in machine learning used to prevent overfitting by adding a penalty term to the model's objective function during training. Overfitting occurs when a model learns the noise and patterns specific to the training data, leading to poor generalization on unseen data. Regularization helps control the complexity of models by penalizing large coefficients or by selecting a subset of features, thus striking the right balance between bias and variance.

    Types of Regularization

    L1 Regularization (Lasso)

    L1 regularization, also known as Lasso (Least Absolute Shrinkage and Selection Operator), adds the absolute value of the magnitude of the coefficients as a penalty term to the loss function. This technique promotes sparsity by driving some coefficient estimates to exactly zero, effectively performing feature selection. The objective function for Lasso is:

    \text{Loss}_{\text{lasso}} = \text{Loss}_{\text{OLS}} + \lambda \sum_{j=1}^{p} |\beta_j|

    where ( \lambda ) is the regularization parameter that controls the strength of regularization.

    GeeksForGeeks
    https://www.geeksforgeeks.org/regularization-in-machine-learning
    Regularization in Machine Learning - GeeksforGeeks
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