How can feature selection improve the accuracy of a predictive model?

Example Concept: Feature selection involves choosing a subset of relevant features for model training, which can lead to improved model performance by eliminating noise and redundant data. Methods like Recursive Feature Elimination (RFE) iteratively remove less important features based on model coefficients, while LASSO regularization adds a penalty to the model that shrinks less important feature coefficients to zero, effectively selecting a subset of features. Tree-based methods, such as those used in Random Forests, provide feature importance scores based on how much each feature improves the model's purity in decision trees.