Machine Learning with AdaBoost on Decision Stumps.

sboost

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)

Installation

Install this package from the CRAN repository.

install.packages("sboost")

Alternatively, use devtools to install the development version of this package.

To install devtools on R run:

install.packages("devtools")

After devtools is installed, to install the sboost package on R run:

devtools::install_github("jadonwagstaff/sboost")

Functions

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.

Author

Jadon Wagstaff

Licence

MIT.