Feature Importance in Tree Models
Feature importance scores provide insights into the data and the model. They help in understanding which features contribute the most to the prediction, aiding in dimensionality reduction and feature selection. This can improve the efficiency and effectiveness of a predictive model.
In tree-based models, feature importance can be derived in several ways:
- Gini Importance (Mean Decrease in Impurity): In Decision Trees and Random Forests, the importance of a feature is often calculated based on the total decrease in node impurity (Gini impurity or entropy) that the feature achieves across all the trees in the forest.
- Mean Decrease in Accuracy: This method involves shuffling the values of each feature and observing the decrease in model accuracy. A significant drop in accuracy indicates high importance of the feature.
- Permutation Importance: Similar to the mean decrease in accuracy, permutation importance measures the change in model performance after randomly permuting the feature values, thus breaking the relationship between the feature and the target.
Understanding Feature Importance and Visualization of Tree Models
Feature importance is a crucial concept in machine learning, particularly in tree-based models. It refers to techniques that assign a score to input features based on their usefulness in predicting a target variable. This article will delve into the methods of calculating feature importance, the significance of these scores, and how to visualize them effectively.
Table of Content
- Feature Importance in Tree Models
- Methods to Calculate Feature Importance
- 1. Decision Tree Feature Importance
- 2. Random Forest Feature Importance
- 3. Permutation Feature Importance
- Demonstrating Visualization of Tree Models
- Yellowbrick for Visualization of Tree Models