https://github.com/dmarcous/cran_deepboost

Deepboost R-package for submission
https://github.com/dmarcous/cran_deepboost

boosting-algorithms decision-trees deep-boosting r

Last synced: 8 months ago
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Deepboost R-package for submission

• Host: GitHub
• URL: https://github.com/dmarcous/cran_deepboost
• Owner: dmarcous
• Created: 2016-02-27T22:11:06.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2017-11-08T18:08:14.000Z (over 8 years ago)
• Last Synced: 2025-10-22T04:58:12.526Z (8 months ago)
• Topics: boosting-algorithms, decision-trees, deep-boosting, r
• Language: C++
• Size: 3.61 MB
• Stars: 19
• Watchers: 4
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
# Deepboost modeling.

[![Travis-CI Build Status](https://travis-ci.org/dmarcous/CRAN_deepboost.svg?branch=master)](https://travis-ci.org/dmarcous/CRAN_deepboost)

[![rstudio mirror downloads](http://cranlogs.r-pkg.org/badges/grand-total/deepboost)](https://github.com/metacran/cranlogs.app)

[![cran version](http://www.r-pkg.org/badges/version/deepboost)](https://CRAN.R-project.org/package=deepboost)

[![codecov.io](https://codecov.io/github/dmarcous/CRAN_deepboost/coverage.svg?branch=master)](https://codecov.io/github/dmarcous/CRAN_deepboost?branch=master)

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](http://jmlr.org/proceedings/papers/v32/cortesb14.pdf) for details

Adapted from Google's C++ deepbbost implementation :

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

## 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 

Yotam Sandbank