OTE: Optimal Trees Ensembles for Regression, Classification and Class Membership Probability Estimation

Functions for creating ensembles of optimal trees for regression, classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019) <doi:10.1007/s11634-019-00364-9>) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) <doi:10.1007/978-3-319-25226-1_34>) 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. Three different methods of tree selection for the case of classification are given. 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.

Version: 1.0.1
Imports: randomForest, stats
Published: 2020-04-20
Author: Zardad Khan, Asma Gul, Aris Perperoglou, Osama Mahmoud, Werner Adler, Miftahuddin and Berthold Lausen
Maintainer: Zardad Khan <zardadkhan at awkum.edu.pk>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: OTE results

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Reference manual: OTE.pdf
Package source: OTE_1.0.1.tar.gz
Windows binaries: r-devel: OTE_1.0.1.zip, r-release: OTE_1.0.1.zip, r-oldrel: OTE_1.0.1.zip
macOS binaries: r-release (arm64): OTE_1.0.1.tgz, r-release (x86_64): OTE_1.0.1.tgz, r-oldrel: OTE_1.0.1.tgz
Old sources: OTE archive

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