Releases

Version 0.2.1

Changes

  • Allows the usage of scikit-learn 0.18.2.
  • Upgrade to latest SMAC version (0.6.0) and latest random forest version (0.6.1).
  • Added a Dockerfile.
  • Added the possibility to change the size of the holdout set when using holdout resampling strategy.
  • Fixed a bug in QDA’s hyperparameters.
  • Typo fixes in print statements.
  • New method to retrieve the models used in the final ensemble.

Contributors

  • Matthias Feurer
  • Katharina Eggensperger
  • Felix Leung
  • caoyi0905
  • Young Ryul Bae
  • Vicente Alencar
  • Lukas Großberger

Version 0.2.0

Major changes

  • auto-sklearn supports custom metrics and all metrics included in scikit-learn. Different metrics can now be passed to the fit()-method estimator objects, for example AutoSklearnClassifier.fit(metric='roc_auc').
  • Upgrade to scikit-learn 0.18.1.
  • Drop XGBoost as the latest release (0.6a2) does not work when spawned by the pyninsher.
  • auto-sklearn can use multiprocessing in calls to predict() and predict_proba. By Laurent Sorber.

Contributors

  • Matthias Feurer
  • Katharina Eggensperger
  • Laurent Sorber
  • Rafael Calsaverini

Version 0.1.x

There are no release notes for auto-sklearn prior to version 0.2.0.

Contributors

  • Matthias Feurer
  • Katharina Eggensperger
  • Aaron Klein
  • Jost Tobias Springenberg
  • Anatolii Domashnev
  • Stefan Falkner
  • Alexander Sapronov
  • Manuel Blum
  • Diego Kobylkin
  • Jaidev Deshpande
  • Jongheon Jeong
  • Hector Mendoza
  • Timothy J Laurent
  • Marius Lindauer
  • _329_
  • Iver Jordal