This library implements a Rcpp based blazingly fast implementation of adaboost.m1 and real adaboost. This will be especially well suited for big datasets. The library currently supports decision trees as the weak classifier. Once the classifiers have been trained, they can be used to predict new datasets. Currently, we support only binary classification task. In addition to calculating the final error, a staged error is also calculated for each additional tree. This can be used to tune the final number of iterations. A plot of the staged error is also generated to help the user decide.
Package details |
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Author | person("Sourav", "Chatterjee", , "souravc83@gmail.com", c("aut", "cre")) |
Maintainer | Sourav Chatterjee <souravc83@gmail.com> |
License | MIT + file LICENSE |
Version | 1.0 |
URL | https://github.com/souravc83/fastBoost |
Package repository | View on GitHub |
Installation |
Install the latest version of this package by entering the following in R:
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