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Overfitting and Tree Pruning
- Overfitting: An induced tree may overfit the training data
- Too many branches, some may reflect anomalies due to noise or outliers
- Poor accuracy for unseen samples
- Two approaches to avoid overfitting
- Prepruning: Halt tree construction early ̵ do not split a node if this would result in the goodness measure falling below a threshold
- Difficult to choose an appropriate threshold
- Postpruning: Remove branches from a “fully grown” tree—get a sequence of progressively pruned trees
- Use a set of data different from the training data to decide which is the “best pruned tree”
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