Decision tree LING 572 Fei Xia 1/16/06 Outline Basic concepts Issues In this lecture, “attribute” and “feature” are interchangeable. Basic concepts Main idea Build a tree decision tree Each node represents a test Training instances are split at each node Greedy algorithm
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In this lecture, “attribute” and “feature” are interchangeable.
Find a decision tree that is as small as possible and fits the data
Q1: Choosing best attribute: what quality measure to use?
Q2: Determining when to stop splitting: avoid overfitting
Q3: Handling continuous attributes
Q4: Handling training data with missing attribute values
Q5: Handing attributes with different costs
Q6: Dealing with continuous goal attribute
H(S)=-i p(ci) log p(ci)
InfoGain (Y | X) = H(Y) – H(Y|X)
(a.k.a. choose the A with the min average entropy)
Where Sa is subset of S for which A has value a.
In practice, both are used. Some people claim that the latter works better than the former.
Question: how to choose split points?
Q1: Choosing best attribute: different quality measures.
Q2: Determining when to stop splitting: stop earlier or post-pruning
Q3: Handling continuous attributes: find the breakpoints
Q4: Handling training data with missing attribute values: blank value, most common value, or fractional count
Q5: Handing attributes with different costs: use a quality measure that includes the cost factors.
Q6: Dealing with continuous goal attribute: various ways of building regression trees.