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Avoiding Overfitting the Data
Given a hypothesis space H, a hypothesis h є H is said to
the training data if there exists some alternative hypothesis h’ є H, such that h’ has smaller error than h’ over the training examples, but h’ has a smaller error than h over the entire distribution of instances. 
ID3 grows each branch of the tree just deeply enough to perfectly classify the training examples.
This can lead to difficulties when there is noise in the data or when the number of training examples is too small to produce a representative sample of the true target function.
Overfitting trees could be produced!
Impact of overfitting
in a typical application of decision tree learning