Random Forests Lightning Talk
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This document discusses using random forests to predict customer conversion for a bank marketing campaign. It begins by introducing the research question of which customers to target for offers. Next, it describes the dataset and explores using decision trees which have high variance and may overfit. Then it introduces random forests as an ensemble method that grows many decision trees to reduce variance and avoid overfitting, providing better predictions. It shows random forests significantly outperform a single decision tree on this dataset and don't require much tuning.
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Random Forests Lightning Talk
- 1. Predicting Customer Conversion with Random Forests A Decision Trees Case Study Daniel Gerlanc, Principal Enplus Advisors, Inc. www.enplusadvisors.com dgerlanc@enplusadvisors.com
- 2. Topics Objectives Research Question Bank Prospect Data Conversion Decision Trees Methods Random Forests Results
- 3. Objective • Which customer or prospects should you call today? • To whom should you offer incentives?
- 4. Dataset • Direct Marketing campaign for bank loans • http://archive.ics.uci.edu/ml/datasets/Ba nk+Marketing • 45211 records, 17 attributes
- 5. Dataset
- 7. Decision Trees Windy Coat yes Sunny No Coat no Coat
- 8. Statistical Decision Trees • Randomness • May not know the relationships ahead of time
- 11. 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.
- 13. How‟d it do? Guessing Precision: 11.7% Decision Tree: 34.8% Actual Predicted no yes no (1) 38,904 (3) 3,444 yes (2) 1,018 (4) 1,845
- 14. Decision Tree Problems • Overfitting the data • High variance • Not globally optimal
- 15. Random Forests One Decision Tree Many Decision Trees (Ensemble)
- 16. Building RF • Sample from the data • At each split, sample from the available variables • Repeat for each tree
- 17. Why more than 1? • Create uncorrelated trees • Reduce variance of predictor • Continual cross-validation
- 18. Random Forests rffit.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
- 19. How‟d it do? Guessing Precision: 11.7% Random Forest: 64.5% Actual Predicted no yes no (1) 38,526 (3) 1396 yes (2) 2748 (4) 2541
- 20. Benefits of RF • Don‟t need a lot of tuning • Don‟t need an extra cross validation step • Many implementations • R, Weka, RapidMiner, Mahout
- 21. References • 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.ht m • 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 - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.
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