Lect23 - Announcement Watch for next homework tomorrow or...

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Announcement Watch for next homework tomorrow or Thursday on web site On Naïve Bayes and Decision Trees 1
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Overfitting Concept performs well on training data (drawn from X according to D ) poorly on unseen examples of interest (same drawing) Excess flexibility in hypothesis space H Finds training set pattern not in population Concept selection from too little (insignificant) training data Confidence in selecting c H Diversity of H reflects our ignorance Training set Z provides information Information(Z) Information need(H) [>> for high confidence] Often Learning algorithms cannot tell With low confidence, expected behavior of “best” concept (on Z) is poor on underlying population Extreme: Rote learning of training data No generalization 2
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Overfitting Statistical significance of inference Decisions with insufficient evidence Complexity of H vs |Z| accuracy On testing On training 3
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Overfitting in Decision Trees A rich set of tests Split to (nearly) homogeneity Accurate on training set Poor expected performance on new data Consider noise (but even w/o…) With limited training examples, deep decision trees are bad Low confidence in lower split choices (why?) Hypothesis space H become too expressive 4
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How to Reduce Overfitting in Decision Trees Restrict the expressiveness of H Limit the set of tests available Limit the depth No deeper than N (say 3 or 12 or 86)
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Lect23 - Announcement Watch for next homework tomorrow or...

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