Classification and Regression Trees

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Classification and Regression Trees. JMP Partition Platform. In the News …. Why? What is it? How does it work? JMP Mechanics Evaluating model? Assessing usefulness? Understanding results Applying results. Analyze > Distribution. Data set > Riding Mowers. Begin with a 1-way analysis.
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Classification and Regression TreesJMP Partition PlatformClassification TreesIn the News …
  • Why?
  • What is it?
  • How does it work?
  • JMP Mechanics
  • Evaluating model?
  • Assessing usefulness?
  • Understanding results
  • Applying results
  • Classification TreesAnalyze > DistributionData set > Riding MowersBegin with a 1-way analysis.There is an equal distribution of values for the two levels of the response variable. Classification TreesWhy?
  • What is it?
  • How does it work?
  • JMP Mechanics
  • Evaluating model?
  • Assessing usefulness?
  • Understanding results
  • Applying results
  • Exploring PredictorsIf we put this in Scatterplot Matrix we are looking for the variable to split to give us best homogeneityClassification TreesHomogeneityIt looks like at an Income of about 85 we would have a “pure” partition only having “owner” records for > 85.Classification TreesLaunch Partition
  • Why?
  • What is it?
  • How does it work?
  • JMP Mechanics
  • Evaluating model?
  • Assessing usefulness?
  • Understanding results
  • Applying results
  • Analyze > Modeling > PartitionIdentify X and Y values in dialog box.Use defaults for everything else at this point.Classification TreesStarting Point | To begin click on SplitAICc = information criterion. Smaller number is better. Looks for a model with a good fit to the truth but with few parameters.G^2 = a likelihood ratio Chi-square; ratio is of expected to observed. Larger value the more likely there is a statistical difference.http://www.brianomeara.info/tutorials/aicClassification TreesFirst SplitClassification TreesSplitting on the Income < 85.5 LeafClassification TreesSplitting at INCOME < 85.5Classification TreesLast Split at Lot Size < 20Classification TreesResult of Splitting on Lot Size < 20Classification TreesSplit HistoryHot Spot > Split HistoryWe can see that the last split did not improve R-SquareClassification TreesShowing Fit Details
  • Why?
  • What is it?
  • How does it work?
  • JMP Mechanics
  • Evaluating model?
  • Assessing usefulness?
  • Understanding results
  • Applying results
  • Focus on misclassification rate and maybe RMSE or Mean Abs Dev.For usefulness focus on the confusion matrix and think about the two types of misclassification.Classification TreesWhy?
  • What is it?
  • How does it work?
  • JMP Mechanics
  • Evaluating model?
  • Assessing usefulness?
  • Understanding results
  • Applying results
  • Importance of PredictorsHigher G^2 > increased importance in predicting outcomeClassification TreesWhy?
  • What is it?
  • How does it work?
  • JMP Mechanics
  • Evaluating model?
  • Assessing usefulness?
  • Understanding results
  • Applying results
  • Hot Spot > Save Columns > Save Prediction FormulaProb(Ownership ==owner)Classification TreesExerciseLost SalesClassification Trees
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