OTE: Optimal Trees Ensembles for Regression, Classification and Class Membership Probability Estimation
Functions for creating ensembles of optimal trees for regression, classification and class membership probability estimation are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Trees are assessed on out-of-bag data and on an independent training data set for individual and collective performance respectively. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data.
- Zardad Khan, Asma Gul, Aris Perperoglou, Osama Mahmoud, Werner Adler, Miftahuddin and Berthold Lausen
- Date of publication
- 2015-07-31 05:26:54
- Zardad Khan <email@example.com>
- GPL (>= 2)
- Exploring Relationships in Body Dimensions
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- Optimal Trees Ensembles for Regression, Classification and...
- Train the ensemble of optimal trees for classification.
- Train the ensemble of optimal trees for class member ship...
- Train the ensemble of optimal trees for regression.
- Prediction function for the object returned by 'OTClass'
- Prediction function for the object returned by 'OTProb'
- Prediction function for the object returned by 'OTReg'
Files in this package