#Deepboost modeling.

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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]