Study
Multiclass classification datasets
4 datasets, 4 tasks, 0 flows, 0 runs
Multiclass classification datasets
4 datasets, 4 tasks, 0 flows, 0 runs
Multiclass classification datasets
4 datasets, 4 tasks, 0 flows, 0 runs
This collection complements the Padding Attacks benchmark datasets serves as a valuable benchmark training set for multi-class classification tasks or detecting information leakage via error code or…
96 datasets, 96 tasks, 0 flows, 0 runs
a gpt ai model for carrtesi
1 datasets, 1 tasks, 0 flows, 0 runs
a gpt ai model for carrtesi
1 datasets, 1 tasks, 0 flows, 0 runs
A set of tasks related to the prediction of online gambling self-exclusion originally appearing in https://doi.org/10.1080/14459795.2013.841721 and https://doi.org/10.1080/14459795.2016.1151913
2 datasets, 2 tasks, 0 flows, 0 runs
This is a collection of datasets that can be used to evaluate Confidence Interval methods for the Generalization Error.The task splits can be ignored.For more information, see…
19 datasets, 19 tasks, 0 flows, 0 runs
Testing FBR
1 datasets, 1 tasks, 0 flows, 0 runs
Testing FBR
1 datasets, 1 tasks, 0 flows, 0 runs
Testing FBR
1 datasets, 1 tasks, 0 flows, 0 runs
Testing FBR
1 datasets, 1 tasks, 0 flows, 0 runs
Testing FBR
1 datasets, 1 tasks, 0 flows, 0 runs
nlp
1 datasets, 1 tasks, 0 flows, 0 runs
nlp
1 datasets, 1 tasks, 0 flows, 0 runs
nlp
1 datasets, 1 tasks, 0 flows, 0 runs
nlp
1 datasets, 1 tasks, 0 flows, 0 runs
nlp
1 datasets, 1 tasks, 0 flows, 0 runs
nlp
1 datasets, 1 tasks, 0 flows, 0 runs
nlp
1 datasets, 1 tasks, 0 flows, 0 runs
Classification/Risk prediction for Tabular data related to MI(Coronary heart disease)
1 datasets, 1 tasks, 0 flows, 0 runs
Classification/Risk prediction for Tabular data related to MI(Coronary heart disease)
1 datasets, 1 tasks, 0 flows, 0 runs
illustrating how to create a benchmark suite
2 datasets, 2 tasks, 0 flows, 0 runs
illustrating how to create a benchmark suite
2 datasets, 2 tasks, 0 flows, 0 runs
illustrating how to create a benchmark suite
2 datasets, 2 tasks, 0 flows, 0 runs
illustrating how to create a benchmark suite
250 datasets, 250 tasks, 0 flows, 0 runs
test
1 datasets, 1 tasks, 0 flows, 0 runs
1)BUY #BANKNIFTY 45300 PE ABOVE -490 TARGET- 40 /70/100/200/250 Point SL-450 2)BUY #BANKNIFTY 45300 PE ABOVE -550 TARGET- 40 /70/100/200/250 Point SL-500 3)BUY#ALKEM 4100 CE ABOVE -218 TARGE- 228,250…
1 datasets, 1 tasks, 0 flows, 0 runs
1)BUY #BANKNIFTY 45300 PE ABOVE -490 TARGET- 40 /70/100/200/250 Point SL-450 2)BUY #BANKNIFTY 45300 PE ABOVE -550 TARGET- 40 /70/100/200/250 Point SL-500 3)BUY#ALKEM 4100 CE ABOVE -218 TARGE- 228,250…
1 datasets, 1 tasks, 0 flows, 0 runs
This collection complements the Timing Attacks benchmark datasets and serves as a valuable training set for multi-class classification tasks or detecting information leakage in OpenSSL. For detailed…
87 datasets, 87 tasks, 0 flows, 0 runs
packet_priority_classification
1 datasets, 1 tasks, 0 flows, 0 runs
packet_priority_classification
1 datasets, 1 tasks, 0 flows, 0 runs
Hard tabular datasets from the TabZilla study.
36 datasets, 36 tasks, 0 flows, 0 runs
Teste
1 datasets, 1 tasks, 0 flows, 0 runs
Teste
1 datasets, 1 tasks, 0 flows, 0 runs
digital_text
1 datasets, 1 tasks, 0 flows, 0 runs
digital_text
1 datasets, 1 tasks, 0 flows, 0 runs
digital_text
1 datasets, 1 tasks, 0 flows, 0 runs
digital_text
1 datasets, 1 tasks, 0 flows, 0 runs
Inclusion Criteria: * There are between 500 and 100000 observations. * There are less than 5000 features after one-hot encoding all categorical features. * The dataset is not in a sparse format. * The…
35 datasets, 35 tasks, 0 flows, 0 runs
We investigate the performance of a wide range of regression algorithms on a wide range of datasets to better understand when they perform well and when they don't. This will yield a meta-dataset that…
3 datasets, 3 tasks, 0 flows, 0 runs
TuningTreesClassification
2 datasets, 2 tasks, 0 flows, 0 runs
TuningTreesClassification
2 datasets, 2 tasks, 0 flows, 0 runs
TuningTreesClassification
2 datasets, 2 tasks, 0 flows, 0 runs
TuningTreesClassification
2 datasets, 2 tasks, 0 flows, 0 runs
We introduce how we configured benchmark datasets to properly evaluate the performance of our proposed method, STCC: Semi-Supervised Learning for Tabular Datasets with Continuous and Categorical…
24 datasets, 24 tasks, 0 flows, 0 runs
this is for test
24 datasets, 24 tasks, 0 flows, 0 runs
Hi there
1 datasets, 1 tasks, 0 flows, 0 runs
Hi there
1 datasets, 1 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "classification on numerical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets have been…
16 datasets, 16 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "regression on numerical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets have been transformed as…
19 datasets, 19 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "regression on both numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets…
17 datasets, 17 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "classification on both numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets…
7 datasets, 7 tasks, 0 flows, 0 runs
Meta Album is a meta-dataset created for few-shot learning, meta-learning, continual learning, AutoML, and more. The Extended version contains the full datasets. Learn more about Meta-Album at…
27 datasets, 27 tasks, 0 flows, 0 runs
Meta Album is a meta-dataset created for few-shot learning, meta-learning, continual learning, AutoML, and more. The Mini version contains 40 randomly selected examples for each class (hence the…
30 datasets, 30 tasks, 0 flows, 0 runs
Meta Album is a meta-dataset created for few-shot learning, meta-learning, continual learning, AutoML, and more. The Micro version is meant for quick experimentation. It only contains 20 randomly…
30 datasets, 30 tasks, 0 flows, 0 runs
2
bot
1 datasets, 1 tasks, 0 flows, 0 runs
2
bot
1 datasets, 1 tasks, 0 flows, 0 runs
2
bot
1 datasets, 1 tasks, 0 flows, 0 runs
2
bot
1 datasets, 1 tasks, 0 flows, 0 runs
a study with no runs attached
0 datasets, 0 tasks, 0 flows, 0 runs
Pange
1 datasets, 1 tasks, 1 flows, 1 runs
Pange
1 datasets, 1 tasks, 1 flows, 1 runs
Suite containing the datasets used in the "classification on numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has…
7 datasets, 7 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "classification on numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has…
7 datasets, 7 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "regression on numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has been…
13 datasets, 13 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "classification on numerical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has been transformed…
15 datasets, 15 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "regression on numerical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has been transformed as…
20 datasets, 20 tasks, 0 flows, 0 runs
An study reporting results of a decision tree with different splitter.
2 datasets, 2 tasks, 1 flows, 2 runs
An example study reporting results of a decision stump.
2 datasets, 2 tasks, 1 flows, 2 runs
An example study reporting results of a decision stump.
1 datasets, 1 tasks, 1 flows, 1 runs
A complimentary set of tasks to the AutoML benchmark that can be used as a training set for meta-learning as suggested by Feurer et al. in the paper "Auto-Sklearn 2.0: Hands-free AutoML via…
208 datasets, 208 tasks, 0 flows, 0 runs
After exploring dierent subsets, the subset consisting of 50% of the total datasets.
29 datasets, 29 tasks, 0 flows, 0 runs
illustrating how to create a benchmark suite
250 datasets, 250 tasks, 0 flows, 0 runs
illustrating how to create a benchmark suite
250 datasets, 250 tasks, 0 flows, 0 runs
Test suite for the Python tutorial on benchmark suites
20 datasets, 20 tasks, 0 flows, 0 runs
Test suite for the Python tutorial on benchmark suites
20 datasets, 20 tasks, 0 flows, 0 runs
Test suite for the Python tutorial on benchmark suites
20 datasets, 20 tasks, 0 flows, 0 runs
Description
2 datasets, 2 tasks, 1 flows, 2 runs
Description
39 datasets, 39 tasks, 1 flows, 39 runs
illustrating how to create a benchmark suite
251 datasets, 251 tasks, 0 flows, 0 runs
case_Classifier for the PHM
1 datasets, 1 tasks, 2 flows, 2 runs
case_Regression for the PHM
1 datasets, 1 tasks, 3 flows, 3 runs
Collection of all classification tasks for the AutoML Benchmark (https://github.com/openml/automlbenchmark).
71 datasets, 71 tasks, 0 flows, 0 runs
Collection of new classification tasks for the AutoML Benchmark (https://github.com/openml/automlbenchmark).
29 datasets, 29 tasks, 0 flows, 0 runs
Collection of regression tasks for the AutoML Benchmark (https://github.com/openml/automlbenchmark).
33 datasets, 33 tasks, 0 flows, 0 runs
15% More difficult and discriminative Percentage of instances with high Discrimination parameter values Dataset :: Percentage of instances vowel :: 92% breast-w :: 92% monks-problems-1 :: 89%…
8 datasets, 8 tasks, 0 flows, 0 runs
10% More difficult and discriminative of the TesteCC18 study Porcentagem de instancias com valores altos do parametro Discriminacao Dataset :: Percentual de instancias banknote-authentication :: 100%…
12 datasets, 12 tasks, 0 flows, 0 runs
Testing how to create a benchmark suite
60 datasets, 60 tasks, 0 flows, 0 runs
Benchmark suite for fair machine learning.
0 datasets, 0 tasks, 0 flows, 0 runs
A benchmark suite to investigate how Deep Learning scales with dataset size. Building upon the prior work from https://openml.github.io/automlbenchmark/
62 datasets, 62 tasks, 0 flows, 0 runs
IRT for regression tasks/datasets
0 datasets, 0 tasks, 0 flows, 0 runs
IRT for classificaion tasks/datasets
0 datasets, 0 tasks, 0 flows, 0 runs
SH on first task of CC18
2 datasets, 2 tasks, 2 flows, 4 runs
SH vs RS on first tasks of CC18
0 datasets, 0 tasks, 0 flows, 0 runs
SH on first task of CC18
0 datasets, 0 tasks, 0 flows, 0 runs
Results from the original AutoML benchmark paper presented in “An Open Source AutoML Benchmark” by Gijsbers et al. at the AutoML workshop at ICML 2019. It contains the results of running several…
19 datasets, 19 tasks, 6 flows, 117 runs
Subset of the OpenML100, with datasets that are friedly towards scikit-learn algorithms (no Imputation or One-hot-encoding necessary)
0 datasets, 0 tasks, 0 flows, 0 runs
Contains currency trading tasks, for various valuta pairs.
192 datasets, 192 tasks, 0 flows, 0 runs
The original set of tasks for the AutoML benchmark presented in “An Open Source AutoML Benchmark” by Gijsbers et al. at the AutoML workshop at ICML 2019. The set of tasks aims to provide a…
39 datasets, 39 tasks, 0 flows, 0 runs