Brent Komer, James Bergstra, and Chris Eliasmith. Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn. In ICML 2014 AutoML Workshop, 8. 2014. URL: https://drive.google.com/viewerng/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWx3c2ljbWwxNHxneDozOGQwOThlNzBiOGFhMDFi.
@inproceedings{komer2014a,
title={Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn},
author={Brent Komer and James Bergstra and Chris Eliasmith},
url={https://drive.google.com/viewerng/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWx3c2ljbWwxNHxneDozOGQwOThlNzBiOGFhMDFi},
year={2014},
pdf={http://compneuro.uwaterloo.ca/files/publications/komer.2014a.pdf},
poster={http://compneuro.uwaterloo.ca/files/publications/komer.2014a.poster.pdf},
booktitle={ICML 2014 AutoML Workshop},
pages={8},
abstract={Hyperopt-sklearn is a new software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of pre-processing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-Newsgroups, Convex Shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and Convex Shapes.},
}