Building a Classification Decision Tree with ID3 and Information Gain

Summary

The article introduces supervised decision trees and explains how the ID3 algorithm builds a classification tree. ID3 starts at the root and greedily selects a feature for each split, aiming to make the resulting groups more homogeneous. The tutorial focuses on categorical classification, distinguishing it from regression trees that predict continuous values.

It develops the split criterion using entropy and information gain, showing how class counts become probabilities and how entropy is calculated for a target and for feature values such as weather conditions in a tennis dataset. Pure subsets have zero entropy; the feature with the greatest information gain is chosen to reduce uncertainty. The article presents MQL5 implementation fragments and a small example dataset, but the supplied text is incomplete and does not establish predictive performance on financial data. ID3's described use is chiefly categorical data, so applying it to trading requires suitable feature treatment and out-of-sample evaluation.

Key ideas

• ID3 builds a classification tree by repeatedly choosing the locally best feature split.
• Entropy measures class uncertainty, while information gain measures the reduction in uncertainty from a split.
• Class frequencies are converted to probabilities to calculate entropy for the target and feature subsets.
• A subset containing only one target class is a pure node with zero entropy.
• The tutorial uses a small categorical example and provides no evidence of trading performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.