= Regression variable selection
{wiki=Category:Regression_variable_selection}
Regression variable selection is the process of identifying and selecting the most relevant predictor variables (or independent variables) to be included in a regression model. The goal is to improve the model's performance by eliminating unnecessary noise introduced by irrelevant or redundant variables, enhancing interpretability, and potentially improving model accuracy. Here are some key aspects of regression variable selection: 1. **Purpose**: The main purposes of variable selection include reducing model complexity, avoiding overfitting, and simplifying the interpretation of the model.
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