cs440-lec10-decision_trees

cs440-lec10-decision_trees - Machine Learning: Decision...

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1 Machine Learning: Machine Learning: Decision Trees Decision Trees Chapter 18.1-18.3 Some material adopted from notes by Chuck Dyer
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2 What is learning? “Learning denotes changes in a system that . .. enable a system to do the same task more efficiently the next time.” –Herbert Simon “Learning is constructing or modifying representations of what is being experienced.” –Ryszard Michalski “Learning is making useful changes in our minds.” –Marvin Minsky
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3 Why learn? Understand and improve efficiency of human learning Discover new things or structure that were previously unknown to humans Examples: data mining, scientific discovery Fill in skeletal or incomplete specifications about a domain Build software agents that can adapt to their users or to other software agents
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4 A general model of learning agents
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5 Major paradigms of machine learning Rote learning – One-to-one mapping from inputs to stored representation. “Learning by memorization.” Association-based storage and retrieval. Induction – Use specific examples to reach general conclusions Clustering – Unsupervised identification of natural groups in data Analogy Determine correspondence between two different representations Discovery – Unsupervised, specific goal not given Genetic algorithms – “Evolutionary” search techniques, based on an analogy to “survival of the fittest” Reinforcement Feedback (positive or negative reward) given at the end of a sequence of steps
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6 The inductive learning problem Extrapolate from a given set of examples to make accurate predictions about future examples Supervised versus unsupervised learning Learn an unknown function f(X) = Y, where X is an input example and Y is the desired output. Supervised learning implies we are given a training set of (X, Y) pairs by a “teacher” Unsupervised learning means we are only given the Xs and some (ultimate) feedback function on our performance. Concept learning or classification Given a set of examples of some concept/class/category, determine if a given example is an instance of the concept or not If it is an instance, we call it a positive example If it is not, it is called a negative example Or we can make a probabilistic prediction (e.g., using a Bayes net)
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7 Supervised concept learning Given a training set of positive and negative examples of a concept Construct a description that will accurately classify whether future examples are positive or negative That is, learn some good estimate of function f given a training set {(x 1 , y 1 ), (x 2 , y 2 ), . .., (x n , y n )} where each y i is either + (positive) or - (negative), or a probability distribution over +/-
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8 Inductive learning framework Raw input data from sensors are typically preprocessed to obtain a feature vector , X, that adequately describes all of the relevant features for classifying examples Each x is a list of (attribute, value) pairs. For example,
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cs440-lec10-decision_trees - Machine Learning: Decision...

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