Decision Trees – the unreasonable power of nested decision rules
Summary
The article explains decision trees using a simple apple/cherry/oak example and covers root/leaf nodes, splitting, entropy, and information gain. It also describes the ID3 algorithm, the Gini impurity alternative, pruning to prevent overfitting, instability concerns, and how random forests mitigate variance, highlighting interpretability and practical relevance in ML education.