Iterative Dichotomiser 3
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
A decision tree learning algorithm proposed by Ross Quinlan in 1986. Core concepts: - Information Entropy: measures the uncertainty of a dataset - Information Gain: the reduction in uncertainty after selecting an attribute. Algorithm steps: 1. Calculate the entropy of the current dataset. 2. For each attribute, calculate the information gain. 3. Select the attribute with the highest information gain as the split node. 4. Recursively build subtrees.
SCAFFOLDING EFFECT
Reduce cognitive load
Greedy selection, fast construction. ID3 greedily selects the best split attribute using the information gain criterion, quickly building a decision tree. Although it may not be globally optimal, it is computationally efficient and is the basis for C4.5.
Anchor fast decisions
A decision tree algorithm proposed by Ross Quinlan in 1986. It uses information entropy to measure dataset uncertainty and uses **information gain** (the reduction in uncertainty after splitting) as the criterion to **greedily** select the best split attribute, recursively building the tree. The mechanism is "local optimum, layer-by-layer approximation," which is computationally efficient, and although not globally optimal, it is the foundation for C4.5 and others.
MINIMUM ACTION
In progress 0/4Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/ID3_algorithmverified
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