Creates classifier for binary outcomes using Freund and Schapire's Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. This type of classifier is nonlinear, but easy to interpret and visualize. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included.
Machine learning package used to build and test classifiers using AdaBoost on decision stumps.
Creates classifier for binary outcomes using Adaptive Boosting (AdaBoost) on decision stumps with a fast C++ implementation. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included. The advantage of this type of classifier is that it is non-linear but it is more interpretable than random forests, neural-nets, and other non-linear classifiers.
See jadonwagstaff.github.io/sboost for a description of how the classifier functions, and what makes this classifier more interpretable than others.
For original paper describing AdaBoost see:
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119-139 (1997)
Install this package from the CRAN repository.
Alternatively, use devtools to install the development version of this package.
To install devtools on R run:
After devtools is installed, to install the sboost package on R run:
sboost - Main machine learning algorithm, uses categorical or continuous features to build a classifier that predicts a binary outcome. Run
?sboost::sboost to see documentation in R.
validate - Uses k-fold cross validation on a training set to validate the classifier.
assess - Shows performance of a classifier on a set of feature vectors and outcomes.
predict - Outputs predictions of a classifier on a set of feature vectors.
Final version of development, ready for CRAN submission.