Introduction to Decision Trees: Understanding Entropy and Information Gain in Machine Learning
By
mschnell
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Summary
This article provides an introduction to decision trees, focusing on entropy and information gain concepts in machine learning. It explains how entropy quantifies the impurity of labeled data points, with pure nodes containing only one class and impure nodes containing multiple classes. The content covers mathematical formulas for calculating entropy as the negative sum of weighted probabilities and demonstrates these concepts through interactive examples for binary classification problems.
Key quotes
· 3 pulledThe total entropy can be written as the negative sum of weighted probabilities
The entropy can be used to quantify the impurity of a collection of labeled data points: a node containing multiple classes is impure whereas a node including only one class is pure
Above, you can compute the entropy of a collection of labeled data points belonging to two classes, which is typical for binary classification problems
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