# output - Output Knowledge representation CS 464...

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1 1 10/06/11 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 2 10/06/11 Output: Knowledge representation Tables Linear models Trees Rules Classification rules Association rules Rules with exceptions More expressive rules Instance-based representation Clusters 3 10/06/11 Output: representing structural patterns Many different ways of representing patterns Decision trees, rules, instance-based, … Also called “knowledge” representation Representation determines inference method Understanding the output is the key to understanding the underlying learning methods Different types of output for different learning problems (e.g. classification, regression, …) 5 10/06/11 Linear models Another simple representation Regression model Inputs (attribute values) and output are all numeric Output is the sum of weighted attribute values The trick is to find good values for the weights

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1 7 10/06/11 Binary classification Line separates the two classes Decision boundary - defines where the decision changes from one class value to the other 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 Boundary becomes a high-dimensional plane ( hyperplane ) when there are multiple attributes Linear models for classification 9 10/06/11 Trees “Divide-and-conquer” approach produces tree Nodes involve testing a particular attribute Usually, attribute value is compared to constant Other possibilities: Comparing values of two attributes Using a function of one or more attributes Leaves assign classification, set of classifications, or probability distribution to instances Unknown instance is routed down the tree 10 10/06/11 Nominal and numeric attributes
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output - Output Knowledge representation CS 464...

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