Lasso regression is a linear regression technique that uses L1 regularization to penalize the absolute size of the coefficients, effectively shrinking some of them to zero, which enables feature selection and promotes sparsity in the model. This helps in preventing overfitting and improving the interpretability of the model by identifying the most significant predictors.

Lasso Regression

Lasso regression

linear regression technique

L1 regularization, also known as Lasso, adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function, promoting sparsity by driving some coefficients to zero. This technique is especially useful for feature selection in high-dimensional datasets, as it can effectively reduce the number of features by eliminating less important ones.

L1 regularization

absolute size of the coefficients

Feature selection is a critical process in machine learning and statistics that involves identifying and selecting a subset of relevant features for model construction. It enhances model performance by reducing overfitting, improving accuracy, and decreasing computation time by eliminating irrelevant or redundant data features.

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