2008-10-30-overfitting - Learning and Overfitting CMPSCI...

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1 CMPSCI 383 October 30, 2008 Learning and Overfitting
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5 Today’s topics Pathologies of learning algorithms Properties of evaluation functions as statistical estimators How evaluation functions and search interact in learning Methods for solving pathologies
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6 Amazing Facts Mutual funds that beat the market! Winning the lottery, twice! Hidden prophesies in text! Amazing coincidences in twins raised apart! Psychic watch repair!
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8 Learning terminology This term refers to a learned structure that expresses a conditional or joint probability distribution. This mathematical expression is used to “score” models. In learning, the evaluation function depends on these two arguments. What is a Model What is an Evaluation Function What are Models and Data
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9 Example problem
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10 Example rule (1) Trains with small closed cars do not carry toxic chemicals
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11 Example rule (2) Trains with with only a small and large car, or a jagged-top car, carry toxic chemicals
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12 How did you devise your rules? Did you… Look for characteristics in one set but missing in the second set? Examine several potential rules? Consider simple rules first? Reject potential rules that didn’t perform well? You performed search
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13 How did you evaluate your rules? Examined how many trains were correctly and incorrectly predicted Assigned lower value to rules which predicted fewer trains correctly. Did you do anything else?
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14 How reliable are these rules? For any given train, how confident are you that the answer is correct? Do we have enough data to construct a reliable rule? How many data points is enough?
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15 Pathologies of Learning Algorithms
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16 Overfitting Adding components to models that reduce performance or leave it unchanged Oversearching Selecting models with lower performance as the size of search space grows Attribute selection errors Preferring attributes with many possible values despite lower performance (Jensen and Cohen 2000) Pathologies of induction algorithms
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17 Sample size (Oates & Jensen 1997, 1998, 1999) Accuracy Tree size Overfitting
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18 Overfitting (continued) (Oates & Jensen 1999)
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