Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
1701.01293
Cited By
v1
v2 (latest)
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
5 January 2017
Giuseppe Casalicchio
Jakob Bossek
Michel Lang
Dominik Kirchhoff
P. Kerschke
B. Hofner
H. Seibold
Joaquin Vanschoren
B. Bischl
VLM
LRM
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"OpenML: An R Package to Connect to the Machine Learning Platform OpenML"
15 / 15 papers shown
Title
Post-Selection Confidence Bounds for Prediction Performance
Pascal Rink
W. Brannath
76
1
0
24 Oct 2022
On the role of benchmarking data sets and simulations in method comparison studies
Sarah Friedrich
T. Friede
56
25
0
02 Aug 2022
Interaction-Grounded Learning with Action-inclusive Feedback
Tengyang Xie
Akanksha Saran
Dylan J. Foster
Lekan Molu
Ida Momennejad
Nan Jiang
Paul Mineiro
John Langford
69
10
0
16 Jun 2022
Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization
Daniel Schalk
B. Bischl
David Rügamer
71
3
0
07 Oct 2021
Test for non-negligible adverse shifts
Vathy M. Kamulete
79
4
0
07 Jul 2021
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach
Christoph Molnar
Gunnar Konig
B. Bischl
Giuseppe Casalicchio
90
84
0
08 Jun 2020
Large-scale benchmark study of survival prediction methods using multi-omics data
Moritz Herrmann
Philipp Probst
R. Hornung
V. Jurinovic
A. Boulesteix
75
80
0
07 Mar 2020
OpenML-Python: an extensible Python API for OpenML
Matthias Feurer
Jan N. van Rijn
Arlind Kadra
Pieter Gijsbers
Neeratyoy Mallik
Sahithya Ravi
Andreas Müller
Joaquin Vanschoren
Frank Hutter
ELM
GP
101
92
0
06 Nov 2019
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers
R. G. Mantovani
André Luis Debiaso Rossi
Edesio Alcobaça
Joaquin Vanschoren
A. Carvalho
52
69
0
04 Jun 2019
Automatic Exploration of Machine Learning Experiments on OpenML
D. Kühn
Philipp Probst
Janek Thomas
B. Bischl
AI4CE
90
21
0
28 Jun 2018
Visualizing the Feature Importance for Black Box Models
Giuseppe Casalicchio
Christoph Molnar
B. Bischl
FAtt
47
183
0
18 Apr 2018
Hyperparameters and Tuning Strategies for Random Forest
Philipp Probst
Marvin N. Wright
A. Boulesteix
168
1,425
0
10 Apr 2018
Tunability: Importance of Hyperparameters of Machine Learning Algorithms
Philipp Probst
B. Bischl
A. Boulesteix
93
626
0
26 Feb 2018
OpenML Benchmarking Suites
B. Bischl
Giuseppe Casalicchio
Matthias Feurer
Pieter Gijsbers
Frank Hutter
Michel Lang
R. G. Mantovani
Jan N. van Rijn
Joaquin Vanschoren
VLM
ELM
132
165
0
11 Aug 2017
To tune or not to tune the number of trees in random forest?
Philipp Probst
A. Boulesteix
81
393
0
16 May 2017
1