Deep Boosting Ensemble Modeling

Provides deep boosting models training, evaluation, predicting and hyper parameter optimising using grid search and cross validation. Based on Google's Deep Boosting algorithm, and Google's C++ implementation. Cortes, C., Mohri, M., & Syed, U. (2014) < http://machinelearning.wustl.edu/mlpapers/papers/icml2014c2_cortesb14>.


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Provides deepboost models training, evaluation, predicting and hyper parameter optimising using grid search and cross validation.

Details

Based on Google's Deep Boosting algorithm by Cortes et al.

See this paper for details

Adapted from Google's C++ deepbbost implementation :

https://github.com/google/deepboost

Another version for the package that uses the original unmodified algorith exists in :

https://github.com/dmarcous/deepboost

Installation

From CRAN :

install.packages("deepboost")

Examples

Choosing parameters for a deepboost model :

best_params <- deepboost.gridSearch(formula, data)

Training a deepboost model :

boost <- deepboost(formula, data,
                    num_iter = best_params[2][[1]], 
                    beta = best_params[3][[1]], 
                    lambda = best_params[4][[1]], 
                    loss_type = best_params[5][[1]]
                    )

Print trained model evaluation statistics :

print(boost)

Classifying using a trained deepboost model :

labels <- predict(boost, newdata)

See Help / demo directory for advanced usage.

Credits

R Package written and maintained by :

Daniel Marcous [email protected]

Yotam Sandbank [email protected]

News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("deepboost")

0.1.6 by Daniel Marcous, a year ago


https://github.com/dmarcous/CRAN_deepboost


Report a bug at https://github.com/dmarcous/CRAN_deepboost/issues


Browse source code at https://github.com/cran/deepboost


Authors: Daniel Marcous [aut, cre] , Yotam Sandbank [aut] , Google Inc. [cph]


Documentation:   PDF Manual  


Apache License (== 2.0) license


Imports Rcpp, methods

Suggests testthat, ada, caret

Linking to Rcpp


See at CRAN