autoxgboost - Automatic tuning and fitting of xgboost.

• Install the development version

  devtools::install_github("ja-thomas/autoxgboost")

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!

Benchmark

Name	Factors	Numerics	Classes	Train instances	Test instances
Dexter	20 000	0	2	420	180
GermanCredit	13	7	2	700	300
Dorothea	100 000	0	2	805	345
Yeast	0	8	10	1 038	446
Amazon	10 000	0	49	1 050	450
Secom	0	591	2	1 096	471
Semeion	256	0	10	1 115	478
Car	6	0	4	1 209	519
Madelon	500	0	2	1 820	780
KR-vs-KP	37	0	2	2 237	959
Abalone	1	7	28	2 923	1 254
Wine Quality	0	11	11	3 425	1 469
Waveform	0	40	3	3 500	1 500
Gisette	5 000	0	2	4 900	2 100
Convex	0	784	2	8 000	50 000
Rot. MNIST + BI	0	784	10	12 000	50 000

Datasets used for the comparison benchmark of autoxgboost, Auto-WEKA and auto-sklearn.

Dataset	baseline	autoxgboost	Auto-WEKA	auto-sklearn
Dexter	52,78	12.22	7.22	5.56
GermanCredit	32.67	27.67	28.33	27.00
Dorothea	6.09	5.22	6.38	5.51
Yeast	68.99	38.88	40.45	40.67
Amazon	99.33	26.22	37.56	16.00
Secom	7.87	7.87	7.87	7.87
Semeion	92.45	8.38	5.03	5.24
Car	29,15	1.16	0.58	0.39
Madelon	50.26	16.54	21.15	12.44
KR-vs-KP	48.96	1.67	0.31	0.42
Abalone	84.04	73.75	73.02	73.50
Wine Quality	55.68	33.70	33.70	33.76
Waveform	68.80	15.40	14.40	14.93
Gisette	50.71	2.48	2.24	1.62
Convex	50.00	22.74	22.05	17.53
Rot. MNIST + BI	88.88	47.09	55.84	46.92

Benchmark results are median percent error across 100 000 bootstrap samples (out of 25 runs) simulating 4 parallel runs. Bold numbers indicate best performing algorithms.

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}
}