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Fferent regularization techniques us to handle multivariate data.

Fferent regularization techniques us to handle multivariate data.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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**Q.1 Explain the different regularization techniques used with Linear regression to handle multivariate data.** In addressing this question, we explore regularization methods like Ridge Regression, Lasso Regression, and Elastic Net, each offering solutions to overfitting when dealing with multivariate data. Ridge Regression minimizes the sum of squared residuals while adding a penalty for large coefficients, which helps in managing multicollinearity. Lasso Regression, on the other hand, uses L1 regularization, promoting sparsity by setting some coefficients to zero, which is useful for feature selection. Elastic Net combines the penalties of both Ridge and Lasso, balancing coefficient shrinkage and sparsity, beneficial in cases with highly correlated predictors.
Transcribed Image Text:**Q.1 Explain the different regularization techniques used with Linear regression to handle multivariate data.** In addressing this question, we explore regularization methods like Ridge Regression, Lasso Regression, and Elastic Net, each offering solutions to overfitting when dealing with multivariate data. Ridge Regression minimizes the sum of squared residuals while adding a penalty for large coefficients, which helps in managing multicollinearity. Lasso Regression, on the other hand, uses L1 regularization, promoting sparsity by setting some coefficients to zero, which is useful for feature selection. Elastic Net combines the penalties of both Ridge and Lasso, balancing coefficient shrinkage and sparsity, beneficial in cases with highly correlated predictors.
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