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Stats 202 - Lecture 7

# 80k no married no 80k yes 10 training data model

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Unformatted text preview: tart from the root of tree. Refund Marital Status No Refund Taxable Income Cheat 80K Married ? 10 Yes No NO MarSt Single, Divorced TaxInc < 80K NO Married NO > 80K YES 8 Applying the Tree Model to Predict the Class for a New Observation Test Data Refund Marital Status No Refund Taxable Income Cheat 80K Married ? 10 Yes No NO MarSt Single, Divorced TaxInc < 80K NO Married NO > 80K YES 9 Applying the Tree Model to Predict the Class for a New Observation Test Data Refund Marital Status No Refund Taxable Income Cheat 80K Married ? 10 Yes No NO MarSt Single, Divorced TaxInc < 80K NO Married NO > 80K YES 10 Applying the Tree Model to Predict the Class for a New Observation Test Data Refund Marital Status No Refund Taxable Income Cheat 80K Married ? 10 Yes No NO MarSt Single, Divorced TaxInc < 80K NO Married NO > 80K YES 11 Applying the Tree Model to Predict the Class for a New Observation Test Data Refund Marital Status No Refund Taxable Income Cheat 80K Married ? 10 Yes No NO MarSt Single, Divorced TaxInc < 80K NO Married NO > 80K YES 12 Applying the Tree Model to Predict the Class for a New Observation Test Data Refund Marital Status No Refund Taxable Income Cheat 80K Married ? 10 Yes No NO MarSt Single, Divorced TaxInc < 80K NO Married Assign Cheat to “No” NO > 80K YES 13 Decision Trees in R The function rpart() in the library “rpart generates decision trees in R. Be careful: This function also does regression trees which are for a numeric response. Make sure the function rpart() knows your class labels are a factor and not a numeric response. (if y is a factor then method="class" is assumed) 14 In class exercise #24: Below is output from the rpart() function. Use this tree to predict the class of the following observations: a) (Age=middle Number=5 Start=10) b) (Age=young Number=2 Start=17) c) (Age=old Number=10 Start=6) 1) root 81 17 absent (0.79012346 0.20987654) 2) Start>=8.5 62 6 absent (0.90322581 0.09677419) 4) Age=old,young 48 2 absent (0.95833333 0.04166667) 8) Start>=13.5 25 0 absent (1.00000000 0.00000000)...
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