Variable selection is the process of identifying and choosing the most relevant features or predictors in a dataset for building a predictive model. This process is crucial because including irrelevant or redundant variables can lead to overfitting, increased complexity, and decreased model interpretability. Effective variable selection enhances model performance and simplifies the interpretation of results by focusing on the most impactful predictors.
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