Relief model

The Relief model is a feature selection technique used in machine learning to identify the most relevant variables for predicting an outcome. It works by randomly selecting a data instance, then finding similar instances with different outcomes (neighbors) and updating the importance of each feature based on how well they distinguish these neighbors. Features that consistently help differentiate between different outcomes are assigned higher scores. This process repeats many times, ultimately highlighting the most impactful features for the predictive model, improving accuracy and efficiency by reducing irrelevant or redundant data.