Relief Algorithm

The Relief algorithm is a feature selection method used in machine learning to identify which variables (features) are most relevant for predicting outcomes. It works by randomly selecting data samples, then comparing each sample's feature values to those of nearby samples—looking at similar and different instances. Features that consistently distinguish between different outcomes are assigned higher importance scores. This process helps filter out irrelevant or less useful features, improving the accuracy and efficiency of predictive models by focusing only on the most informative variables.