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. The prediction functions return estimates of the test responses/class labels and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. for the test data are also returned. Three different methods for tree selection are given for the case of classification.
| Package: | OTE |
| Type: | Package |
| Version: | 1.0.1 |
| Date: | 2020-04-18 |
| License: | GPL-3 |
Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). Ensemble of optimal trees, random forest and random projection ensemble classification. Advances in Data Analysis and Classification, 1-20.