Feature selection methods: Backward elimination, forward selection, and LASSO
Feature selection is an essential part of building efficient machine learning models. By selecting the most relevant features, you can improve model performance, reduce overfitting, and enhance interpretability. This reading will describe three common techniques for feature selection: backward elimination, forward selection, and least absolute shrinkage and selection operator (LASSO). These methods help identify which features are the most significant for a given model and discard irrelevant ones.
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Introduction
Feature selection is an essential part of building efficient machine learning models. By selecting the most relevant features, you can improve model performance, reduce overfitting, and enhance interpretability.
This reading will describe three common techniques for feature selection: backward elimination, forward selection, and least absolute shrinkage and selection operator (LASSO). These methods help identify which features are the most significant for a given model and discard irrelevant ones.
