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Predicting Customer Conversion with Random Forests
 

Predicting Customer Conversion with Random Forests

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Talk given for New England Artificial Intelligence on October 10, 2012.

Talk given for New England Artificial Intelligence on October 10, 2012.

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Predicting Customer Conversion with Random Forests Predicting Customer Conversion with Random Forests Presentation Transcript

  • Predicting Customer Conversionwith Random ForestsA Decision Trees Case StudyDaniel Gerlanc, PrincipalEnplus Advisors, Inc.www.enplusadvisors.comdgerlanc@enplusadvisors.com
  • TopicsObjectives Research Question Bank Prospect Data Conversion Decision TreesMethods Random Forests Results
  • Objective• Which customer or prospects should you call today?• To whom should you offer incentives?
  • Dataset• Direct Marketing campaign for bank loans• http://archive.ics.uci.edu/ml/datasets/Ba nk+Marketing• 45211 records, 17 features
  • Dataset
  • Decision Trees
  • Decision Trees Windy Coat yesSunny No Coat no Coat
  • Statistical Decision Trees• Randomness• May not know the relationships ahead of time
  • Decision Trees
  • SplittingDeterministic process
  • Decision Tree Code tree.1 <- rpart(takes.loan ~ ., data=bank)• See the „rpart‟ and „rpart.plot‟ R packages.• Many parameters available to control the fit.
  • Make Predictionspredict(tree.1, type=“vector”)
  • How‟d it do? Naïve Accuracy: 11.7% Decision Tree Precision: 34.8% ActualPredicted no yesno (1) 38,904 (3) 3,444yes (2) 1,018 (4) 1,845
  • Decision Tree Problems• Overfitting the data (high variance)• May not use all relevant features
  • Random ForestsOne Decision Tree Many Decision Trees (Ensemble)
  • Building RF• Sample from the data• At each split, sample from the available variables• Repeat for each tree
  • Motivations for RF• Create uncorrelated trees• Variance reduction• Subspace exploration
  • Random Forestsrffit.1 <- randomForest(takes.loan ~ ., data=bank)Most important parameters are: Variable Description Default ntree Number of Trees 500 mtry Number of variables to randomly • square root of # predictors for select at each node classification • # predictors / 3 for regression
  • How‟d it do?Naïve Accuracy: 11.7%Random Forest • Precision: 64.5% (2541 / 3937) • Recall: 48% (2541 / 5289) ActualPredicted yes noyes (1) 2,541 (3) 2748no (2) 1,396 (4) 38,526
  • Tuning RFrffit.1 <- tuneRF(X, y, mtryStart=1, stepFactor=2,improve=0.05)
  • Benefits of RF• Good accuracy with default settings• Relatively easy to make parallel• Many implementations • R, Weka, RapidMiner, Mahout
  • References• A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18--22.• Breiman, Leo. Classification and Regression Trees. Belmont, Calif: Wadsworth International Group, 1984. Print.• Brieman, Leo and Adele Cutler. Random forests. http://www.stat.berkeley.edu/~breiman/RandomForests/cc_contact.htm• S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.