How do decision trees play a role in feature selection?
- Decision trees select the ‘best’ feature for splitting at each node based on information gain.
- Information gain measures the reduction in entropy (disorder) in a set of data points.
- Features with higher information gain are considered more important for splitting, thus aiding in feature selection.
- By recursively selecting features for splitting, decision trees inherently prioritize the most relevant features for the model.
Feature selection using Decision Tree
Feature selection using decision trees involves identifying the most important features in a dataset based on their contribution to the decision tree’s performance. The article aims to explore feature selection using decision trees and how decision trees evaluate feature importance.