README.md

In ja-thomas/autoxgboost: Automatic tuning and fitting of xgboost

autoxgboost - Automatic tuning and fitting of xgboost.

• Install the development version

  splus 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}
}

ja-thomas/autoxgboost documentation built on April 9, 2020, 11:10 p.m.