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OpenML: An R Package to Connect to the Machine Learning Platform OpenML
v1v2 (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
    VLMLRM
ArXiv (abs)PDFHTML

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
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
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
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
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
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
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
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
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
ELMGP
101
92
0
06 Nov 2019
A meta-learning recommender system for hyperparameter tuning: predicting
  when tuning improves SVM classifiers
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
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
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
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
Tunability: Importance of Hyperparameters of Machine Learning Algorithms
Philipp Probst
B. Bischl
A. Boulesteix
93
626
0
26 Feb 2018
OpenML Benchmarking Suites
OpenML Benchmarking Suites
B. Bischl
Giuseppe Casalicchio
Matthias Feurer
Pieter Gijsbers
Frank Hutter
Michel Lang
R. G. Mantovani
Jan N. van Rijn
Joaquin Vanschoren
VLMELM
132
165
0
11 Aug 2017
To tune or not to tune the number of trees in random forest?
To tune or not to tune the number of trees in random forest?
Philipp Probst
A. Boulesteix
81
393
0
16 May 2017
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