Version 0.4.0

  • Fixes #409: fixes predict_proba to no longer raise an AttributeError.
  • Improved documentation of the parallel example.
  • Classifiers are now tested to be idempotent as required by scikit-learn.
  • Fixes the usage of the shrinkage parameter in LDA.
  • Fixes #410 and changes the SGD hyperparameters
  • Fixes #425 which caused the non-linear support vector machine to always crash on OSX.
  • Implements #149: it is now possible to pass a custom cross-validation split following scikit-learn’s model_selection module.
  • It is now possible to decide whether or not to shuffle the data in Auto-sklearn by passing a bool shuffle in the dictionary of resampling_strategy_arguments.
  • Added functionality to track the test performance over time.
  • Re-factored the ensemble building to be faster, read less data from the hard drive and perform random tie breaking in case of equally well-performing models.
  • Implements #438: To be consistent with the output of SMAC (which minimizes the loss of a target function), the output of the ensemble builder is now also the output of a minimization problem.
  • Implements #271: XGBoost is available again, even configuring the new dropout functionality.
  • New documentation section inspecting the results.
  • Fixes #444: Auto-sklearn now only loads models for refit which are actually relevant for the ensemble.
  • Adds an operating system check at import and installation time to make sure to not accidentaly run on a Windows machine.
  • New examples gallery using sphinx gallery:
  • Safeguard Auto-sklearn against deleting directories it did not create (Issue #317.


  • Matthias Feurer
  • kaa
  • Josh Mabry
  • Katharina Eggensperger
  • Vladimir Glazachev
  • Jesper van Engelen
  • Jin Woo Ahn
  • Enrico Testa
  • Marius Lindauer
  • Yassine Morakakam

Version 0.3.0

  • Upgrade to scikit-learn 0.19.1.
  • Do not use the DummyClassifier or DummyRegressor as part of an ensemble. Fixes #140.
  • Fixes #295 by loading the data in the subprocess instead of the main process.
  • Fixes #326: refitting could result in a type error. This is now fixed by better type checking in the classification components.
  • Updated search space for RandomForestClassifier, ExtraTreesClassifier and GradientBoostingClassifier (fixes #358).
  • Removal of constant features is now a part of the pipeline.
  • Allow passing an SMBO object into the AutoSklearnClassifier and AutoSklearnRegressor.


  • Matthias Feurer
  • Jesper van Engelen

Version 0.2.1

  • 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.


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

Version 0.2.0

  • 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'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.


  • 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.


  • 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