Feature selection is a process of selecting relevant and informative variables for a machine learning model, with the aim of improving the accuracy and generalisability of the model. Instead of using all available variables, the most relevant features are selected to reduce computational cost and improve model interpretation. Feature selection techniques include statistical, correlation and feature importance methods, among others. It is a technique commonly used in data pre-processing for machine learning.
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