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Machine Learning 3 - Decision Tree Learning

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The document discusses decision tree learning and the ID3 algorithm. It covers topics like decision tree representation, entropy and information gain for selecting attributes, overfitting, and techniques to avoid overfitting like reduced error pruning. It also discusses handling continuous values, missing data, and attributes with many values or costs in decision tree learning.

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Machine Learning Chapter 3. Decision Tree Learning Tom M. Mitchell
Abstract ,[object Object],[object Object],[object Object],[object Object]
Decision Tree for PlayTennis
A Tree to Predict C-Section Risk ,[object Object],[object Object]
Decision Trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
When to Consider Decision Trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Top-Down Induction of Decision Trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Entropy(1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Entropy(2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Information Gain ,[object Object]
Training Examples
Selecting the Next Attribute(1/2) ,[object Object]
Selecting the Next Attribute(2/2) S sunny = {D1,D2,D8,D9,D11} Gain (S sunny , Humidity) = .970 - (3/5) 0.0 - (2/5) 0.0 = .970 Gain (S sunny , Temperature) = .970 - (2/5) 0.0 - (2/5) 1.0 - (1/5) 0.0 = .570 Gain (S sunny , Wind) = .970 - (2/5) 1.0 - (3/5) .918 = .019
Hypothesis Space Search by ID3(1/2)
Hypothesis Space Search by ID3(2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inductive Bias in ID3 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Occam’s Razor ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overfitting in Decision Trees ,[object Object],[object Object],[object Object]
Overfitting ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overfitting in Decision Tree Learning
Avoiding Overfitting ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Reduced-Error Pruning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Effect of Reduced-Error Pruning
Rule Post-Pruning ,[object Object],[object Object],[object Object],[object Object]
Converting A Tree to Rules ,[object Object],[object Object],[object Object],[object Object],[object Object]
Continuous Valued Attributes ,[object Object],[object Object],[object Object]
Attributes with Many Values ,[object Object],[object Object],[object Object],[object Object],[object Object]
Attributes with Costs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Unknown Attribute Values ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Machine Learning 3 - Decision Tree Learning