Feature Importance

Feature importance refers to the techniques used to determine which factors or variables in a dataset contribute most significantly to a model's predictions. In simpler terms, it identifies which pieces of information are the most influential in helping a system make decisions. For example, if a model predicts house prices, features like location, size, and number of bedrooms might have high importance, while others, like the color of the front door, might matter less. Understanding feature importance helps refine models and ensures that the most critical factors are considered.