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This paper shows how a meta-learning technique can be applied to decide when to prune, how much pruning is appropriate and what the best pruning technique is for a given learning task. The meta-learning technique uses unpruned decision trees and information from decision tree construction to describe the learning tasks. Results concerning two decision tree pruning strategies (Quinlan''s error-based pruning and a cost-complexity pruning) show that the technique improves overall accuracy. The paper suggests that induction on the connection between tasks and biases is the way to decide about the convenience of the different simplicity biases.

God doesn't always shave with Occam's razor — Learning when and how to prune