The ID3 (Iterative Dichotomiser 3) algorithm serves as one of the foundational pillars upon which decision tree learning is built. Developed by Ross Quinlan in the 1980s, ID3 remains a fundamental algorithm, forming the basis for subsequent tree-based methods like C4.5 and CART (Classification and Regression Trees).

The ID3 algorithm constructs a decision tree by recursively splitting the dataset based on the attribute that provides the highest information gain. Here’s a step-by-step breakdown: Entropy measures the impurity or randomness in the dataset. where is the proportion of instances belonging to class .

Developed by Ross Quinlan in the 1980s, ID3 remains a fundamental algorithm, forming the basis for subsequent tree-based methods like C4.5 and CART (Classification and Regression Trees). Machine learning models called decision trees divide the input data recursively according to features to arrive at a decision.

How ID3 works — Using entropy and information gain to make the best splits. ✅ Step-by-step implementation — Writing a decision tree in Python from scratch. ✅ Evaluating our model — Making sure our tree isn’t just memorizing data. ✅ Real-world applications — From spam filters to medical diagnoses, decision trees are everywhere!