48 citations · 79 across the 22 of their papers we have counts for
4 papers · 1 filter
Automatic Gradient Boosting
Janek Thomas, Stefan Coors, Bernd Bischl
Automatic machine learning performs predictive modeling with high performing machine learning tools without human interference. This is achieved by making machine learning applicat…
Automatic Exploration of Machine Learning Experiments on OpenML
Daniel Kühn, Philipp Probst, Janek Thomas +1
Understanding the influence of hyperparameters on the performance of a machine learning algorithm is an important scientific topic in itself and can help to improve automatic hyper…
Visualizing the Feature Importance for Black Box Models
Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models have been developed. We introduc…
Tunability: Importance of Hyperparameters of Machine Learning Algorithms
Philipp Probst, Bernd Bischl, Anne-Laure Boulesteix
Modern supervised machine learning algorithms involve hyperparameters that have to be set before running them. Options for setting hyperparameters are default values from the softw…