README.md

In ja-thomas/autoxgboostMC: Automatic Preprocessing, Fitting and Tuning for xgboost using multiple criteria.

autoxgboostMC - Multiple-Criteria tuning and fitting of xgboost models.

• Installing the development version

  splus devtools::install_github("ja-thomas/autoxgboostMC")

General overview

autoxgboost aims to find an optimal xgboost model automatically using the machine learning framework mlr and the bayesian optimization framework mlrMBO.

Work in progress!

AutoxgboostMC embraces R6 for a cleaner design. See the example code below for the new API.

# Instantiate the object with a list of measures to optimize.
axgb = AutoxgboostMC$new(pid.task, measures = list(auc, timepredict))
# Set hyperparameters
axgb$set_nthread(2L)
# Fit on a Task
axgb$fit(time.budget = 5L)
p = axgb$predict(pid.task)

autoxgboost - How to Cite

The Automatic Gradient Boosting framework was presented at the ICML/IJCAI-ECAI 2018 AutoML Workshop (poster). Please cite our ICML AutoML workshop paper on arxiv. You can get citation info via citation("autoxgboost") or copy the following BibTex entry:

@inproceedings{autoxgboost,
  title={Automatic Gradient Boosting},
  author={Thomas, Janek and Coors, Stefan and Bischl, Bernd},
  booktitle={International Workshop on Automatic Machine Learning at ICML},
  year={2018}
}

ja-thomas/autoxgboostMC documentation built on May 17, 2019, 4:22 a.m.