#Deepboost modeling.
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 dmarcous@gmail.com
Yotam Sandbank yotamsandbank@gmail.com