Feature Importance from Trees
Feature importance from trees is a machine learning technique used to quantify the contribution of input features to the predictive performance of tree-based models, such as decision trees, random forests, and gradient boosting machines. It helps identify which features are most influential in making predictions by measuring metrics like Gini impurity reduction or mean decrease in accuracy. This concept is widely applied in model interpretation, feature selection, and understanding data relationships in domains like finance, healthcare, and marketing.
Developers should learn this concept when working with tree-based models to improve model transparency, perform feature selection to reduce overfitting, and gain insights into data patterns for business decisions. It is particularly useful in scenarios requiring explainable AI, such as credit scoring or medical diagnosis, where understanding feature contributions is critical for trust and compliance.