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Subsampling of the dataset eye_movements (44130) with seed=2 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 21 features - 2 classes - 0 missing values
Subsampling of the dataset electricity (44156) with seed=2 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 9 features - 2 classes - 0 missing values
Subsampling of the dataset electricity (44156) with seed=3 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 9 features - 2 classes - 0 missing values
Subsampling of the dataset electricity (44156) with seed=4 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 9 features - 2 classes - 0 missing values
Subsampling of the dataset eye_movements (44157) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 24 features - 2 classes - 0 missing values
Subsampling of the dataset eye_movements (44157) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 24 features - 2 classes - 0 missing values
Subsampling of the dataset Higgs (44129) with seed=4 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 25 features - 2 classes - 0 missing values
Subsampling of the dataset eye_movements (44130) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 21 features - 2 classes - 0 missing values
Subsampling of the dataset eye_movements (44130) with seed=3 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 21 features - 2 classes - 0 missing values
Subsampling of the dataset albert (41147) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 79 features - 2 classes - 12826 missing values
Subsampling of the dataset albert (41147) with seed=2 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 79 features - 2 classes - 12888 missing values
Subsampling of the dataset albert (41147) with seed=3 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 79 features - 2 classes - 12678 missing values
Subsampling of the dataset dionis (41167) with seed=4 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 61 features - 355 classes - 0 missing values
Subsampling of the dataset albert (41147) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 79 features - 2 classes - 13059 missing values
Subsampling of the dataset dionis (41167) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 61 features - 355 classes - 0 missing values
Subsampling of the dataset dionis (41167) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 61 features - 355 classes - 0 missing values
Subsampling of the dataset dionis (41167) with seed=2 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 61 features - 355 classes - 0 missing values
Subsampling of the dataset dionis (41167) with seed=3 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 61 features - 355 classes - 0 missing values
Subsampling of the dataset gina (41158) with seed=3 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed:…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset gina (41158) with seed=4 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed:…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset ozone-level-8hr (1487) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 73 features - 2 classes - 0 missing values
Subsampling of the dataset ozone-level-8hr (1487) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 73 features - 2 classes - 0 missing values
Subsampling of the dataset albert (41147) with seed=4 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 79 features - 2 classes - 12697 missing values
Subsampling of the dataset gina (41158) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed:…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset vehicle (54) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed:…
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846 instances - 19 features - 4 classes - 0 missing values
Subsampling of the dataset vehicle (54) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed:…
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846 instances - 19 features - 4 classes - 0 missing values
Subsampling of the dataset vehicle (54) with seed=2 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed:…
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846 instances - 19 features - 4 classes - 0 missing values
Subsampling of the dataset vehicle (54) with seed=3 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed:…
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846 instances - 19 features - 4 classes - 0 missing values
Subsampling of the dataset gina (41158) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed:…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset gina (41158) with seed=2 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed:…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset ozone-level-8hr (1487) with seed=2 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 73 features - 2 classes - 0 missing values
Subsampling of the dataset ozone-level-8hr (1487) with seed=3 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 73 features - 2 classes - 0 missing values
Subsampling of the dataset ozone-level-8hr (1487) with seed=4 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample(…
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2000 instances - 73 features - 2 classes - 0 missing values
Synchronous motors (SMs) are AC motors with constant speed. Synchronous machine data were obtained in real time from the experimental operating environment. The task is to estimate the excitation…
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557 instances - 5 features - 0 classes - 0 missing values
This data set measures the running time of a matrix-matrix product A\*B = C, where all matrices have size 2048 x 2048, using a parameterizable SGEMM GPU kernel with 241600 possible parameter…
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241600 instances - 15 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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55319 instances - 736 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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5465575 instances - 12 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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188318 instances - 125 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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8885 instances - 256 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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4177 instances - 9 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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52031 instances - 5 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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1000000 instances - 6 features - 0 classes - 0 missing values
A copy of MD_MIX_Mini dataset from Meta album Set0
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28240 instances - 69 features - 706 classes - 665053 missing values
Amazon Reviews data (data source) The repository has several datasets. For this case study, we are using the Electronics dataset.
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10000 instances - 3 features - classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original…
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16599 instances - 17 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original…
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8885 instances - 43 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original…
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4177 instances - 8 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical…
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24780 instances - 8 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical…
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111762 instances - 33 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical…
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4966 instances - 12 features - 2 classes - 0 missing values
This synthetic dataset contains demographic and clinical data used to train a classifier to predict a diagnosis (of schizophrenia or depression) and assess the model performance for intersectional…
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11000 instances - 20 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original…
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5465575 instances - 9 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical…
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58252 instances - 32 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical…
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13272 instances - 22 features - 2 classes - 0 missing values
This is a "supervised learning" challenge in machine learning. We are making available 30 datasets, all pre-formatted in given feature representations (this means that each example consists of a fixed…
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2984 instances - 145 features - 0 classes - 0 missing values
This dataset classifies people described by a set of attributes as good or bad credit risks.This dataset comes with a cost matrix:Good Bad (predicted) Good 0 1 (actual)Bad 5 0 It is worse to class a…
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1000 instances - 21 features - 2 classes - 0 missing values
test
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48842 instances - 15 features - 2 classes - 6465 missing values
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
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1473 instances - 10 features - 0 classes - 0 missing values
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
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1473 instances - 10 features - 0 classes - 0 missing values
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
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1473 instances - 10 features - 0 classes - 0 missing values
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
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1473 instances - 10 features - 0 classes - 0 missing values
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
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1473 instances - 10 features - 0 classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-massive.html#munin) - Number of nodes: 1041 - Number of arcs: 1397 - Number of parameters: 80592 -…
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5000 instances - 1041 features - classes - 0 missing values
PASS is a large-scale image dataset that does not include any humans and which can be used for high-quality pretraining while significantly reducing privacy concerns. Upload by OpenML team.
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1439588 instances - 7 features - 94137 classes - 1775490 missing values
DATA
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3119345 instances - 88 features - classes - 24532528 missing values
Botnet dataset, can be used for device profiling, attack detection, and detection classification
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3668522 instances - 47 features - classes - 0 missing values
We introduce AfriSenti, which consists of 14 sentiment datasets of 110,000+ tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese,…
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111720 instances - 4 features - 3 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
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14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
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14 instances - 5 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX GBP/USD from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to…
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1834 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/TRY from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to…
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1832 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/GBP from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to…
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1835 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX CAD/JPY from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to…
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43825 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/NOK from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to…
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43825 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/HKD from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/JPY from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to…
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43825 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/HUF from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX USD/CAD from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/SEK from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX AUD/SGD from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to…
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43825 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX AUD/SGD from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to…
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1832 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX USD/CHF from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/SGD from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to…
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43825 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX AUD/JPY from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/TRY from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX AUD/CHF from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/TRY from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to…
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43825 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/TRY from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to…
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43825 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/USD from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to…
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43825 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/AUD from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values