Provides deepboost models training, evaluation, predicting and hyper parameter optimising using grid search and cross validation.
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
From CRAN :
install.packages("deepboost")
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.
R Package written and maintained by :
Daniel Marcous dmarcous@gmail.com
Yotam Sandbank yotamsandbank@gmail.com
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.