Computer scienceData scienceMachine learningClassificationDecision tree learning

ID3 algorithm

Theory

Reduce the chaos!

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Imagine you have a dataset that contains different samples of cactuses, your task is to predict cactus type. You found entropy values for each feature. It's time to make the first split!

Below you find the set of entropy values, calculate the information gain (IG) for each feature. Then insert the IG value of the feature you'd pick for a split.

E(X)=0.76E(X∣Ai)={0.25,0.53,0,0.32,0.67}E(X) = 0.76 \newline E(X|A_i) = \{0.25, 0.53, 0, 0.32, 0.67\}

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