This repository contains the source code for an RMarkdown document that compares various variable selection techniques on simulated data. The document is deployed on RPubs and can be accessed here.
The project aims to compare different variable selection techniques, including Lasso, Ridge, and Elastic Net, on simulated data. The comparison is performed using R, a popular language for statistical computing and graphics.
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Lasso (Least Absolute Shrinkage and Selection Operator): Lasso is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
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Ridge Regression: Ridge regression is a method used in machine learning to add a degree of bias to the regression estimates, which reduces the standard errors.
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Elastic Net: Elastic Net is a hybrid of Lasso and Ridge Regression techniques. It is trained with L1 and L2 prior as regularizer. Elastic-net is useful when there are multiple features which are correlated.
The RMarkdown document provides a detailed comparison of these techniques, including their strengths, weaknesses, and appropriate use cases. It also includes code snippets and visualizations to aid understanding.
To run the RMarkdown document locally, you will need to have R and RStudio installed on your machine. You can then clone this repository and open the .Rmd file in RStudio.
To run the RMarkdown document locally, you will need to have R and RStudio installed on your machine. You can then clone this repository and open the .Rmd file in RStudio.
Contributions are welcome. Please open an issue to discuss your ideas or submit a pull request with your changes.