Feature Selection
The process of identifying and retaining the most relevant and informative input variables in a dataset while discarding redundant or noisy data attributes.
Think of It Like This
Like packing for a trip and only bringing the clothes you will actually wear, leaving behind the heavy items that will just weigh your suitcase down.
Feature selection reduces the dimensionality of the data, which helps prevent overfitting and significantly decreases training time. Methods can be categorized into filter, wrapper, and embedded techniques. By removing irrelevant variables, models become much simpler, more interpretable, and often achieve higher accuracy on unseen data.