'Computing information gain' diaporamas de présentation

Chapter 6. Classification and Prediction

What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation. Support Vector Machines (SVM) Associative classification

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Decision Trees

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Decision Trees

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Inferring rudimentary rules

Inferring rudimentary rules. 1R: learns a 1-level decision tree I.e., rules that all test one particular attribute Basic version One branch for each value Each branch assigns most frequent class

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Machine Learning in Practice Lecture 17

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Classification

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Continuous Attributes: Computing GINI Index / 2

Continuous Attributes: Computing GINI Index / 2. Measure of Impurity: Entropy. Computing Entropy of a Single Node. Computing information Gain After Splitting. Problems with Information Gain. Gain Ratio. Measure of Impurity: Classification Error. Computing Error of a Single Node.

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