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CS 464: Introduction to
Machine Learning
Output:
Knowledge representation
Slides for Chapter 3 adapted from
http://www.cs.waikato.ac.nz/ml/weka/book.html
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Output: Knowledge representation
●
Tables
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Linear models
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Trees
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Rules
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Classification rules
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Association rules
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Rules with exceptions
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More expressive rules
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Instancebased representation
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Clusters
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Output: representing structural patterns
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Many different ways of representing patterns
♦
Decision trees, rules, instancebased, …
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Also called “knowledge” representation
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Representation determines inference method
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Understanding the output is the key to understanding the
underlying learning methods
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Different types of output for different learning problems
(e.g. classification, regression, …)
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Linear models
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Another simple representation
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Regression model
♦
Inputs (attribute values) and output are all numeric
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Output is the sum of weighted attribute values
♦
The trick is to find good values for the weights
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Binary classification
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Line
separates
the two classes
♦
Decision boundary  defines where the decision
changes from one class value to the other
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Prediction is made by plugging in observed values of
the attributes into the expression
♦
Predict one class if output
≥
0, and the other class if
output < 0
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Boundary becomes a highdimensional plane
(
hyperplane
) when there are multiple attributes
Linear models for classification
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Trees
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“Divideandconquer” approach produces tree
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Nodes involve testing a particular attribute
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Usually, attribute value is compared to constant
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Other possibilities:
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Comparing values of two attributes
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Using a function of one or more attributes
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Leaves assign classification, set of classifications,
or probability distribution to instances
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Unknown instance is routed down the tree
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Nominal and numeric attributes
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 Spring '11
 NoProfessor
 Linear Regression, Regression Analysis, Machine Learning, PRP

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