Class Project Showcase: Overfitting in Machine Learning

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A presentation for course 91.543 Artificial Intelligence I took in Fall 2008

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Class Project Showcase: Overfitting in Machine Learning

  1. 1. By Beibei Yang and Zheng FangJanuary 7, 2009 1
  2. 2. Introduction Machine Learning: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E ith i E. Overfitting: Given a hypothesis space H, a hypothesis h H is said to overfit the training data if there exists some alternative hypothesis h’ H, H such th t h h smaller error th h’ over th t i i examples, b t h that has ll than the training l but h’ has smaller error than h over the entire distribution of instances. Decision tree: (aka classification trees or regression trees) In these tree structures, leaves represent classifications and b t t t l t l ifi ti d branches represent h t conjunctions of features that lead to those classifications. Our implementation used the ID3 algorithm, which uses the entropy and gain of each node t create a classification t i f h d to t l ifi ti tree. January 7, 2009 2
  3. 3. Cause of Overfitting (1) Lack of training data Standard vs. Insufficiency January 7, 2009 3
  4. 4. Cause of Overfitting (2) Biased data Standard vs. Bias January 7, 2009 4

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