Datasets:
asingh15
/
openml

Dataset card Data Studio Files
xet
 Community
1
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2
364k
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30 values
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13k
⌀
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__index_level_0__
int64
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3.8k
362,626
predictive_accuracy
accuracy_score
covertype_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset covertype (44121) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - X1 (numeric)], 1: [1 - X2 (numeric)], 2: [2 - X3 (numeric)], 3: [3 - X4 (numeric)], 4: [4 - X5 (numeric)], 5: [5 - X6 (numeric)], 6: [6 - X7 (numeric)], 7: [7 - X8 (numeric)], 8: [8 - X9 (numeric)], 9: [9 - X10 (numeric)], 10: [10 - Y (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 1.0...
covertype_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" ]
[ false, false, false, false, false, false, false, false, false, false ]
3,045
362,642
predictive_accuracy
accuracy_score
MagicTelescope_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset MagicTelescope (44125) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_ma...
{0: [0 - fLength: (numeric)], 1: [1 - fWidth: (numeric)], 2: [2 - fSize: (numeric)], 3: [3 - fConc: (numeric)], 4: [4 - fConc1: (numeric)], 5: [5 - fAsym: (numeric)], 6: [6 - fM3Long: (numeric)], 7: [7 - fM3Trans: (numeric)], 8: [8 - fAlpha: (numeric)], 9: [9 - fDist: (numeric)], 10: [10 - class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 1.0...
MagicTelescope_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "fLength:", "fWidth:", "fSize:", "fConc:", "fConc1:", "fAsym:", "fM3Long:", "fM3Trans:", "fAlpha:", "fDist:" ]
[ false, false, false, false, false, false, false, false, false, false ]
3,046
362,609
predictive_accuracy
accuracy_score
california_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset california (44090) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: i...
{0: [0 - MedInc (numeric)], 1: [1 - HouseAge (numeric)], 2: [2 - AveRooms (numeric)], 3: [3 - AveBedrms (numeric)], 4: [4 - Population (numeric)], 5: [5 - AveOccup (numeric)], 6: [6 - Latitude (numeric)], 7: [7 - Longitude (numeric)], 8: [8 - price (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 1.0, ...
california_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "MedInc", "HouseAge", "AveRooms", "AveBedrms", "Population", "AveOccup", "Latitude", "Longitude" ]
[ false, false, false, false, false, false, false, false ]
3,047
362,650
predictive_accuracy
accuracy_score
bank-marketing_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset bank-marketing (44126) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_ma...
{0: [0 - V1 (numeric)], 1: [1 - V6 (numeric)], 2: [2 - V10 (numeric)], 3: [3 - V12 (numeric)], 4: [4 - V13 (numeric)], 5: [5 - V14 (numeric)], 6: [6 - V15 (numeric)], 7: [7 - Class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 8.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 1.0, ...
bank-marketing_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V6", "V10", "V12", "V13", "V14", "V15" ]
[ false, false, false, false, false, false, false ]
3,048
362,634
predictive_accuracy
accuracy_score
house_16H_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset house_16H (44123) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - P1 (numeric)], 1: [1 - P5p1 (numeric)], 2: [2 - P6p2 (numeric)], 3: [3 - P11p4 (numeric)], 4: [4 - P14p9 (numeric)], 5: [5 - P15p1 (numeric)], 6: [6 - P15p3 (numeric)], 7: [7 - P16p2 (numeric)], 8: [8 - P18p2 (numeric)], 9: [9 - P27p4 (numeric)], 10: [10 - H2p2 (numeric)], 11: [11 - H8p2 (numeric)],...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 17.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 16.0, 'NumberOfSymbolicFeatures': 1.0...
house_16H_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "P1", "P5p1", "P6p2", "P11p4", "P14p9", "P15p1", "P15p3", "P16p2", "P18p2", "P27p4", "H2p2", "H8p2", "H10p1", "H13p1", "H18pA", "H40p4" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,049
362,637
predictive_accuracy
accuracy_score
kdd_ipums_la_97-small_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset kdd_ipums_la_97-small (44124) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, ncla...
{0: [0 - value (numeric)], 1: [1 - rent (numeric)], 2: [2 - ftotinc (numeric)], 3: [3 - momloc (numeric)], 4: [4 - famsize (numeric)], 5: [5 - nchild (numeric)], 6: [6 - eldch (numeric)], 7: [7 - yngch (numeric)], 8: [8 - nsibs (numeric)], 9: [9 - age (numeric)], 10: [10 - occscore (numeric)], 11: [11 - sei ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0...
kdd_ipums_la_97-small_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "value", "rent", "ftotinc", "momloc", "famsize", "nchild", "eldch", "yngch", "nsibs", "age", "occscore", "sei", "inctot", "incwage", "incbus", "incfarm", "incss", "incwelfr", "incother", "poverty" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,050
362,625
predictive_accuracy
accuracy_score
covertype_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset covertype (44121) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - X1 (numeric)], 1: [1 - X2 (numeric)], 2: [2 - X3 (numeric)], 3: [3 - X4 (numeric)], 4: [4 - X5 (numeric)], 5: [5 - X6 (numeric)], 6: [6 - X7 (numeric)], 7: [7 - X8 (numeric)], 8: [8 - X9 (numeric)], 9: [9 - X10 (numeric)], 10: [10 - Y (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 1.0...
covertype_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" ]
[ false, false, false, false, false, false, false, false, false, false ]
3,051
362,641
predictive_accuracy
accuracy_score
kdd_ipums_la_97-small_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset kdd_ipums_la_97-small (44124) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, ncla...
{0: [0 - value (numeric)], 1: [1 - rent (numeric)], 2: [2 - ftotinc (numeric)], 3: [3 - momloc (numeric)], 4: [4 - famsize (numeric)], 5: [5 - nchild (numeric)], 6: [6 - eldch (numeric)], 7: [7 - yngch (numeric)], 8: [8 - nsibs (numeric)], 9: [9 - age (numeric)], 10: [10 - occscore (numeric)], 11: [11 - sei ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0...
kdd_ipums_la_97-small_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "value", "rent", "ftotinc", "momloc", "famsize", "nchild", "eldch", "yngch", "nsibs", "age", "occscore", "sei", "inctot", "incwage", "incbus", "incfarm", "incss", "incwelfr", "incother", "poverty" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,052
362,648
predictive_accuracy
accuracy_score
bank-marketing_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset bank-marketing (44126) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_ma...
{0: [0 - V1 (numeric)], 1: [1 - V6 (numeric)], 2: [2 - V10 (numeric)], 3: [3 - V12 (numeric)], 4: [4 - V13 (numeric)], 5: [5 - V14 (numeric)], 6: [6 - V15 (numeric)], 7: [7 - Class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 8.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 1.0, ...
bank-marketing_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V6", "V10", "V12", "V13", "V14", "V15" ]
[ false, false, false, false, false, false, false ]
3,053
362,438
predictive_accuracy
accuracy_score
kdd_ipums_la_97-small
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 numerical features" benchmark. Original description: **Author**: **Source**: Unknown - Date unknown **Please cite**: Binarized version of the o...
{0: [0 - value (nominal)], 1: [1 - rent (nominal)], 2: [2 - ftotinc (nominal)], 3: [3 - momloc (nominal)], 4: [4 - famsize (nominal)], 5: [5 - nchild (numeric)], 6: [6 - eldch (numeric)], 7: [7 - yngch (numeric)], 8: [8 - nsibs (numeric)], 9: [9 - age (numeric)], 10: [10 - occscore (numeric)], 11: [11 - sei ...
{'MajorityClassSize': 2594.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 2594.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 5188.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 15.0, 'NumberOfSymbolicFeatures': 5.0...
kdd_ipums_la_97-small
[ "value", "rent", "ftotinc", "momloc", "famsize", "nchild", "eldch", "yngch", "nsibs", "age", "occscore", "sei", "inctot", "incwage", "incbus", "incfarm", "incss", "incwelfr", "incother", "poverty" ]
[ true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,054
362,623
predictive_accuracy
accuracy_score
covertype_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset covertype (44121) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - X1 (numeric)], 1: [1 - X2 (numeric)], 2: [2 - X3 (numeric)], 3: [3 - X4 (numeric)], 4: [4 - X5 (numeric)], 5: [5 - X6 (numeric)], 6: [6 - X7 (numeric)], 7: [7 - X8 (numeric)], 8: [8 - X9 (numeric)], 9: [9 - X10 (numeric)], 10: [10 - Y (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 1.0...
covertype_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" ]
[ false, false, false, false, false, false, false, false, false, false ]
3,055
362,636
predictive_accuracy
accuracy_score
house_16H_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset house_16H (44123) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - P1 (numeric)], 1: [1 - P5p1 (numeric)], 2: [2 - P6p2 (numeric)], 3: [3 - P11p4 (numeric)], 4: [4 - P14p9 (numeric)], 5: [5 - P15p1 (numeric)], 6: [6 - P15p3 (numeric)], 7: [7 - P16p2 (numeric)], 8: [8 - P18p2 (numeric)], 9: [9 - P27p4 (numeric)], 10: [10 - H2p2 (numeric)], 11: [11 - H8p2 (numeric)],...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 17.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 16.0, 'NumberOfSymbolicFeatures': 1.0...
house_16H_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "P1", "P5p1", "P6p2", "P11p4", "P14p9", "P15p1", "P15p3", "P16p2", "P18p2", "P27p4", "H2p2", "H8p2", "H10p1", "H13p1", "H18pA", "H40p4" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,056
362,640
predictive_accuracy
accuracy_score
kdd_ipums_la_97-small_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset kdd_ipums_la_97-small (44124) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, ncla...
{0: [0 - value (numeric)], 1: [1 - rent (numeric)], 2: [2 - ftotinc (numeric)], 3: [3 - momloc (numeric)], 4: [4 - famsize (numeric)], 5: [5 - nchild (numeric)], 6: [6 - eldch (numeric)], 7: [7 - yngch (numeric)], 8: [8 - nsibs (numeric)], 9: [9 - age (numeric)], 10: [10 - occscore (numeric)], 11: [11 - sei ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0...
kdd_ipums_la_97-small_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "value", "rent", "ftotinc", "momloc", "famsize", "nchild", "eldch", "yngch", "nsibs", "age", "occscore", "sei", "inctot", "incwage", "incbus", "incfarm", "incss", "incwelfr", "incother", "poverty" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,057
362,645
predictive_accuracy
accuracy_score
MagicTelescope_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset MagicTelescope (44125) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_ma...
{0: [0 - fLength: (numeric)], 1: [1 - fWidth: (numeric)], 2: [2 - fSize: (numeric)], 3: [3 - fConc: (numeric)], 4: [4 - fConc1: (numeric)], 5: [5 - fAsym: (numeric)], 6: [6 - fM3Long: (numeric)], 7: [7 - fM3Trans: (numeric)], 8: [8 - fAlpha: (numeric)], 9: [9 - fDist: (numeric)], 10: [10 - class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 1.0...
MagicTelescope_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "fLength:", "fWidth:", "fSize:", "fConc:", "fConc1:", "fAsym:", "fM3Long:", "fM3Trans:", "fAlpha:", "fDist:" ]
[ false, false, false, false, false, false, false, false, false, false ]
3,058
362,638
predictive_accuracy
accuracy_score
kdd_ipums_la_97-small_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset kdd_ipums_la_97-small (44124) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, ncla...
{0: [0 - value (numeric)], 1: [1 - rent (numeric)], 2: [2 - ftotinc (numeric)], 3: [3 - momloc (numeric)], 4: [4 - famsize (numeric)], 5: [5 - nchild (numeric)], 6: [6 - eldch (numeric)], 7: [7 - yngch (numeric)], 8: [8 - nsibs (numeric)], 9: [9 - age (numeric)], 10: [10 - occscore (numeric)], 11: [11 - sei ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0...
kdd_ipums_la_97-small_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "value", "rent", "ftotinc", "momloc", "famsize", "nchild", "eldch", "yngch", "nsibs", "age", "occscore", "sei", "inctot", "incwage", "incbus", "incfarm", "incss", "incwelfr", "incother", "poverty" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,059
362,653
predictive_accuracy
accuracy_score
phoneme_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset phoneme (44127) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int ...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - Class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
phoneme_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V2", "V3", "V4", "V5" ]
[ false, false, false, false, false ]
3,060
362,633
predictive_accuracy
accuracy_score
house_16H_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset house_16H (44123) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - P1 (numeric)], 1: [1 - P5p1 (numeric)], 2: [2 - P6p2 (numeric)], 3: [3 - P11p4 (numeric)], 4: [4 - P14p9 (numeric)], 5: [5 - P15p1 (numeric)], 6: [6 - P15p3 (numeric)], 7: [7 - P16p2 (numeric)], 8: [8 - P18p2 (numeric)], 9: [9 - P27p4 (numeric)], 10: [10 - H2p2 (numeric)], 11: [11 - H8p2 (numeric)],...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 17.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 16.0, 'NumberOfSymbolicFeatures': 1.0...
house_16H_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "P1", "P5p1", "P6p2", "P11p4", "P14p9", "P15p1", "P15p3", "P16p2", "P18p2", "P27p4", "H2p2", "H8p2", "H10p1", "H13p1", "H18pA", "H40p4" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,062
362,632
predictive_accuracy
accuracy_score
house_16H_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset house_16H (44123) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - P1 (numeric)], 1: [1 - P5p1 (numeric)], 2: [2 - P6p2 (numeric)], 3: [3 - P11p4 (numeric)], 4: [4 - P14p9 (numeric)], 5: [5 - P15p1 (numeric)], 6: [6 - P15p3 (numeric)], 7: [7 - P16p2 (numeric)], 8: [8 - P18p2 (numeric)], 9: [9 - P27p4 (numeric)], 10: [10 - H2p2 (numeric)], 11: [11 - H8p2 (numeric)],...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 17.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 16.0, 'NumberOfSymbolicFeatures': 1.0...
house_16H_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "P1", "P5p1", "P6p2", "P11p4", "P14p9", "P15p1", "P15p3", "P16p2", "P18p2", "P27p4", "H2p2", "H8p2", "H10p1", "H13p1", "H18pA", "H40p4" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,063
362,643
predictive_accuracy
accuracy_score
MagicTelescope_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset MagicTelescope (44125) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_ma...
{0: [0 - fLength: (numeric)], 1: [1 - fWidth: (numeric)], 2: [2 - fSize: (numeric)], 3: [3 - fConc: (numeric)], 4: [4 - fConc1: (numeric)], 5: [5 - fAsym: (numeric)], 6: [6 - fM3Long: (numeric)], 7: [7 - fM3Trans: (numeric)], 8: [8 - fAlpha: (numeric)], 9: [9 - fDist: (numeric)], 10: [10 - class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 1.0...
MagicTelescope_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "fLength:", "fWidth:", "fSize:", "fConc:", "fConc1:", "fAsym:", "fM3Long:", "fM3Trans:", "fAlpha:", "fDist:" ]
[ false, false, false, false, false, false, false, false, false, false ]
3,064
362,646
predictive_accuracy
accuracy_score
MagicTelescope_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset MagicTelescope (44125) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_ma...
{0: [0 - fLength: (numeric)], 1: [1 - fWidth: (numeric)], 2: [2 - fSize: (numeric)], 3: [3 - fConc: (numeric)], 4: [4 - fConc1: (numeric)], 5: [5 - fAsym: (numeric)], 6: [6 - fM3Long: (numeric)], 7: [7 - fM3Trans: (numeric)], 8: [8 - fAlpha: (numeric)], 9: [9 - fDist: (numeric)], 10: [10 - class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 1.0...
MagicTelescope_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "fLength:", "fWidth:", "fSize:", "fConc:", "fConc1:", "fAsym:", "fM3Long:", "fM3Trans:", "fAlpha:", "fDist:" ]
[ false, false, false, false, false, false, false, false, false, false ]
3,065
3,932
predictive_accuracy
accuracy_score
anthracyclineTaxaneChemotherapy
**Author**: **Source**: Unknown - Date unknown **Please cite**: Data from the RSCTC 2010 Discovery Challenge. All datasets contain between 100 and 400 samples, characterized by values of 20,000 - 65,000 attributes. Samples are assigned to several (2-10) classes. All attributes are numeric and represent measure...
{0: [0 - Var1 (numeric)], 1: [1 - Var2 (numeric)], 2: [2 - Var3 (numeric)], 3: [3 - Var4 (numeric)], 4: [4 - Var5 (numeric)], 5: [5 - Var6 (numeric)], 6: [6 - Var7 (numeric)], 7: [7 - Var8 (numeric)], 8: [8 - Var9 (numeric)], 9: [9 - Var10 (numeric)], 10: [10 - Var11 (numeric)], 11: [11 - Var12 (numeric)], ...
{'MajorityClassSize': 95.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 64.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 61360.0, 'NumberOfInstances': 159.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 61359.0, 'NumberOfSymbolicFeatures': 1....
anthracyclineTaxaneChemotherapy
[ "Var1", "Var2", "Var3", "Var4", "Var5", "Var6", "Var7", "Var8", "Var9", "Var10", "Var11", "Var12", "Var13", "Var14", "Var15", "Var16", "Var17", "Var18", "Var19", "Var20", "Var21", "Var22", "Var23", "Var24", "Var25", "Var26", "Var27", "Var28", "Var29", "Var30...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
3,066
362,652
predictive_accuracy
accuracy_score
phoneme_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset phoneme (44127) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int ...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - Class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
phoneme_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V2", "V3", "V4", "V5" ]
[ false, false, false, false, false ]
3,067
362,656
predictive_accuracy
accuracy_score
phoneme_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset phoneme (44127) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int ...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - Class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
phoneme_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V2", "V3", "V4", "V5" ]
[ false, false, false, false, false ]
3,068
362,655
predictive_accuracy
accuracy_score
phoneme_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset phoneme (44127) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int ...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - Class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
phoneme_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V2", "V3", "V4", "V5" ]
[ false, false, false, false, false ]
3,069
362,651
predictive_accuracy
accuracy_score
bank-marketing_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset bank-marketing (44126) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_ma...
{0: [0 - V1 (numeric)], 1: [1 - V6 (numeric)], 2: [2 - V10 (numeric)], 3: [3 - V12 (numeric)], 4: [4 - V13 (numeric)], 5: [5 - V14 (numeric)], 6: [6 - V15 (numeric)], 7: [7 - Class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 8.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 1.0, ...
bank-marketing_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V6", "V10", "V12", "V13", "V14", "V15" ]
[ false, false, false, false, false, false, false ]
3,070
362,635
predictive_accuracy
accuracy_score
house_16H_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset house_16H (44123) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - P1 (numeric)], 1: [1 - P5p1 (numeric)], 2: [2 - P6p2 (numeric)], 3: [3 - P11p4 (numeric)], 4: [4 - P14p9 (numeric)], 5: [5 - P15p1 (numeric)], 6: [6 - P15p3 (numeric)], 7: [7 - P16p2 (numeric)], 8: [8 - P18p2 (numeric)], 9: [9 - P27p4 (numeric)], 10: [10 - H2p2 (numeric)], 11: [11 - H8p2 (numeric)],...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 17.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 16.0, 'NumberOfSymbolicFeatures': 1.0...
house_16H_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "P1", "P5p1", "P6p2", "P11p4", "P14p9", "P15p1", "P15p3", "P16p2", "P18p2", "P27p4", "H2p2", "H8p2", "H10p1", "H13p1", "H18pA", "H40p4" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,071
362,654
predictive_accuracy
accuracy_score
phoneme_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset phoneme (44127) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int ...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - Class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
phoneme_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V2", "V3", "V4", "V5" ]
[ false, false, false, false, false ]
3,072
362,630
predictive_accuracy
accuracy_score
pol_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset pol (44122) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10...
{0: [0 - f5 (numeric)], 1: [1 - f6 (numeric)], 2: [2 - f7 (numeric)], 3: [3 - f8 (numeric)], 4: [4 - f9 (numeric)], 5: [5 - f13 (numeric)], 6: [6 - f14 (numeric)], 7: [7 - f15 (numeric)], 8: [8 - f16 (numeric)], 9: [9 - f17 (numeric)], 10: [10 - f18 (numeric)], 11: [11 - f19 (numeric)], 12: [12 - f20 (numer...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 27.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 26.0, 'NumberOfSymbolicFeatures': 1.0...
pol_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "f5", "f6", "f7", "f8", "f9", "f13", "f14", "f15", "f16", "f17", "f18", "f19", "f20", "f21", "f22", "f23", "f24", "f25", "f26", "f27", "f28", "f29", "f30", "f31", "f32", "f33" ]
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,073
362,644
predictive_accuracy
accuracy_score
MagicTelescope_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset MagicTelescope (44125) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_ma...
{0: [0 - fLength: (numeric)], 1: [1 - fWidth: (numeric)], 2: [2 - fSize: (numeric)], 3: [3 - fConc: (numeric)], 4: [4 - fConc1: (numeric)], 5: [5 - fAsym: (numeric)], 6: [6 - fM3Long: (numeric)], 7: [7 - fM3Trans: (numeric)], 8: [8 - fAlpha: (numeric)], 9: [9 - fDist: (numeric)], 10: [10 - class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 11.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 1.0...
MagicTelescope_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "fLength:", "fWidth:", "fSize:", "fConc:", "fConc1:", "fAsym:", "fM3Long:", "fM3Trans:", "fAlpha:", "fDist:" ]
[ false, false, false, false, false, false, false, false, false, false ]
3,074
362,649
predictive_accuracy
accuracy_score
bank-marketing_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset bank-marketing (44126) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_ma...
{0: [0 - V1 (numeric)], 1: [1 - V6 (numeric)], 2: [2 - V10 (numeric)], 3: [3 - V12 (numeric)], 4: [4 - V13 (numeric)], 5: [5 - V14 (numeric)], 6: [6 - V15 (numeric)], 7: [7 - Class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 8.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 1.0, ...
bank-marketing_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V6", "V10", "V12", "V13", "V14", "V15" ]
[ false, false, false, false, false, false, false ]
3,075
362,679
predictive_accuracy
accuracy_score
electricity_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset electricity (44156) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: ...
{0: [0 - date (numeric)], 1: [1 - day (nominal)], 2: [2 - period (numeric)], 3: [3 - nswprice (numeric)], 4: [4 - nswdemand (numeric)], 5: [5 - vicprice (numeric)], 6: [6 - vicdemand (numeric)], 7: [7 - transfer (numeric)], 8: [8 - class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 2.0, ...
electricity_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "date", "day", "period", "nswprice", "nswdemand", "vicprice", "vicdemand", "transfer" ]
[ false, true, false, false, false, false, false, false ]
3,076
362,682
predictive_accuracy
accuracy_score
electricity_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
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( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: ...
{0: [0 - date (numeric)], 1: [1 - day (nominal)], 2: [2 - period (numeric)], 3: [3 - nswprice (numeric)], 4: [4 - nswdemand (numeric)], 5: [5 - vicprice (numeric)], 6: [6 - vicdemand (numeric)], 7: [7 - transfer (numeric)], 8: [8 - class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 2.0, ...
electricity_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "date", "day", "period", "nswprice", "nswdemand", "vicprice", "vicdemand", "transfer" ]
[ false, true, false, false, false, false, false, false ]
3,077
362,669
predictive_accuracy
accuracy_score
eye_movements_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset eye_movements (44130) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - prevFixDur (numeric)], 3: [3 - firstfixDur (numeric)], 4: [4 - firstPassFixDur (numeric)], 5: [5 - nextFixDur (numeric)], 6: [6 - firstSaccLen (numeric)], 7: [7 - lastSaccLen (numeric)], 8: [8 - prevFixPos (numeric)], 9: [9 - landingPos (numeric)],...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0...
eye_movements_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lineNo", "assgNo", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "regressDur", "pupilDiamMax", "pupilDiamLag", "timePrtctg", "titleNo", "wordNo" ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,078
362,697
predictive_accuracy
accuracy_score
rl_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset rl (44160) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10,...
{0: [0 - V1 (numeric)], 1: [1 - V5 (numeric)], 2: [2 - V6 (numeric)], 3: [3 - V8 (nominal)], 4: [4 - V14 (nominal)], 5: [5 - V15 (nominal)], 6: [6 - V17 (nominal)], 7: [7 - V18 (nominal)], 8: [8 - V19 (nominal)], 9: [9 - V20 (numeric)], 10: [10 - V21 (numeric)], 11: [11 - V22 (nominal)], 12: [12 - class (no...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 13.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 8.0,...
rl_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V5", "V6", "V8", "V14", "V15", "V17", "V18", "V19", "V20", "V21", "V22" ]
[ false, false, false, true, true, true, true, true, true, false, false, true ]
3,079
362,678
predictive_accuracy
accuracy_score
electricity_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset electricity (44156) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: ...
{0: [0 - date (numeric)], 1: [1 - day (nominal)], 2: [2 - period (numeric)], 3: [3 - nswprice (numeric)], 4: [4 - nswdemand (numeric)], 5: [5 - vicprice (numeric)], 6: [6 - vicdemand (numeric)], 7: [7 - transfer (numeric)], 8: [8 - class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 2.0, ...
electricity_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "date", "day", "period", "nswprice", "nswdemand", "vicprice", "vicdemand", "transfer" ]
[ false, true, false, false, false, false, false, false ]
3,080
362,449
predictive_accuracy
accuracy_score
kdd_ipums_la_97-small
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 numerical features" benchmark. Original description: **Author**: **Source**: Unknown - Date unknown **Please cite**: Binarized version of the o...
{0: [0 - value (nominal)], 1: [1 - rent (nominal)], 2: [2 - ftotinc (nominal)], 3: [3 - momloc (nominal)], 4: [4 - famsize (nominal)], 5: [5 - nchild (numeric)], 6: [6 - eldch (numeric)], 7: [7 - yngch (numeric)], 8: [8 - nsibs (numeric)], 9: [9 - age (numeric)], 10: [10 - occscore (numeric)], 11: [11 - sei ...
{'MajorityClassSize': 2594.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 2594.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 5188.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 15.0, 'NumberOfSymbolicFeatures': 5.0...
kdd_ipums_la_97-small
[ "value", "rent", "ftotinc", "momloc", "famsize", "nchild", "eldch", "yngch", "nsibs", "age", "occscore", "sei", "inctot", "incwage", "incbus", "incfarm", "incss", "incwelfr", "incother", "poverty" ]
[ true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,081
362,671
predictive_accuracy
accuracy_score
eye_movements_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
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( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - prevFixDur (numeric)], 3: [3 - firstfixDur (numeric)], 4: [4 - firstPassFixDur (numeric)], 5: [5 - nextFixDur (numeric)], 6: [6 - firstSaccLen (numeric)], 7: [7 - lastSaccLen (numeric)], 8: [8 - prevFixPos (numeric)], 9: [9 - landingPos (numeric)],...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0...
eye_movements_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lineNo", "assgNo", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "regressDur", "pupilDiamMax", "pupilDiamLag", "timePrtctg", "titleNo", "wordNo" ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,082
362,673
predictive_accuracy
accuracy_score
eye_movements_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset eye_movements (44130) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - prevFixDur (numeric)], 3: [3 - firstfixDur (numeric)], 4: [4 - firstPassFixDur (numeric)], 5: [5 - nextFixDur (numeric)], 6: [6 - firstSaccLen (numeric)], 7: [7 - lastSaccLen (numeric)], 8: [8 - prevFixPos (numeric)], 9: [9 - landingPos (numeric)],...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0...
eye_movements_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lineNo", "assgNo", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "regressDur", "pupilDiamMax", "pupilDiamLag", "timePrtctg", "titleNo", "wordNo" ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,083
362,681
predictive_accuracy
accuracy_score
electricity_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
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( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: ...
{0: [0 - date (numeric)], 1: [1 - day (nominal)], 2: [2 - period (numeric)], 3: [3 - nswprice (numeric)], 4: [4 - nswdemand (numeric)], 5: [5 - vicprice (numeric)], 6: [6 - vicdemand (numeric)], 7: [7 - transfer (numeric)], 8: [8 - class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 2.0, ...
electricity_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "date", "day", "period", "nswprice", "nswdemand", "vicprice", "vicdemand", "transfer" ]
[ false, true, false, false, false, false, false, false ]
3,084
362,680
predictive_accuracy
accuracy_score
electricity_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
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( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: ...
{0: [0 - date (numeric)], 1: [1 - day (nominal)], 2: [2 - period (numeric)], 3: [3 - nswprice (numeric)], 4: [4 - nswdemand (numeric)], 5: [5 - vicprice (numeric)], 6: [6 - vicdemand (numeric)], 7: [7 - transfer (numeric)], 8: [8 - class (nominal)]}
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 2.0, ...
electricity_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "date", "day", "period", "nswprice", "nswdemand", "vicprice", "vicdemand", "transfer" ]
[ false, true, false, false, false, false, false, false ]
3,085
362,670
predictive_accuracy
accuracy_score
eye_movements_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
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( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - prevFixDur (numeric)], 3: [3 - firstfixDur (numeric)], 4: [4 - firstPassFixDur (numeric)], 5: [5 - nextFixDur (numeric)], 6: [6 - firstSaccLen (numeric)], 7: [7 - lastSaccLen (numeric)], 8: [8 - prevFixPos (numeric)], 9: [9 - landingPos (numeric)],...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0...
eye_movements_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lineNo", "assgNo", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "regressDur", "pupilDiamMax", "pupilDiamLag", "timePrtctg", "titleNo", "wordNo" ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,086
362,668
predictive_accuracy
accuracy_score
eye_movements_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
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( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - prevFixDur (numeric)], 3: [3 - firstfixDur (numeric)], 4: [4 - firstPassFixDur (numeric)], 5: [5 - nextFixDur (numeric)], 6: [6 - firstSaccLen (numeric)], 7: [7 - lastSaccLen (numeric)], 8: [8 - prevFixPos (numeric)], 9: [9 - landingPos (numeric)],...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 1.0...
eye_movements_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lineNo", "assgNo", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "regressDur", "pupilDiamMax", "pupilDiamLag", "timePrtctg", "titleNo", "wordNo" ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,087
362,130
predictive_accuracy
accuracy_score
odds
odds wins losses
{0: [0 - odds in favor (numeric)], 1: [1 - odds against (numeric)], 2: [2 - result (string)], 3: [3 - Unnamed: 3 (string)], 4: [4 - Unnamed: 4 (string)], 5: [5 - Unnamed: 5 (string)], 6: [6 - Unnamed: 6 (string)], 7: [7 - Unnamed: 7 (string)], 8: [8 - Unnamed: 8 (string)]}
{'MajorityClassSize': 189.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 2.0, 'NumberOfClasses': 4.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 297.0, 'NumberOfInstancesWithMissingValues': 296.0, 'NumberOfMissingValues': 1790.0, 'NumberOfNumericFeatures': 2.0, 'NumberOfSymbolicFeatures': 0.0, ...
odds
[ "odds in favor", "odds against", "Unnamed: 3", "Unnamed: 4", "Unnamed: 5", "Unnamed: 6", "Unnamed: 7", "Unnamed: 8" ]
[ false, false, false, false, false, false, false, false ]
3,088
362,695
predictive_accuracy
accuracy_score
rl_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset rl (44160) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10,...
{0: [0 - V1 (numeric)], 1: [1 - V5 (numeric)], 2: [2 - V6 (numeric)], 3: [3 - V8 (nominal)], 4: [4 - V14 (nominal)], 5: [5 - V15 (nominal)], 6: [6 - V17 (nominal)], 7: [7 - V18 (nominal)], 8: [8 - V19 (nominal)], 9: [9 - V20 (numeric)], 10: [10 - V21 (numeric)], 11: [11 - V22 (nominal)], 12: [12 - class (no...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 13.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 8.0,...
rl_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V5", "V6", "V8", "V14", "V15", "V17", "V18", "V19", "V20", "V21", "V22" ]
[ false, false, false, true, true, true, true, true, true, false, false, true ]
3,089
362,694
predictive_accuracy
accuracy_score
rl_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset rl (44160) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10,...
{0: [0 - V1 (numeric)], 1: [1 - V5 (numeric)], 2: [2 - V6 (numeric)], 3: [3 - V8 (nominal)], 4: [4 - V14 (nominal)], 5: [5 - V15 (nominal)], 6: [6 - V17 (nominal)], 7: [7 - V18 (nominal)], 8: [8 - V19 (nominal)], 9: [9 - V20 (numeric)], 10: [10 - V21 (numeric)], 11: [11 - V22 (nominal)], 12: [12 - class (no...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 13.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 8.0,...
rl_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V5", "V6", "V8", "V14", "V15", "V17", "V18", "V19", "V20", "V21", "V22" ]
[ false, false, false, true, true, true, true, true, true, false, false, true ]
3,090
362,693
predictive_accuracy
accuracy_score
rl_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset rl (44160) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10,...
{0: [0 - V1 (numeric)], 1: [1 - V5 (numeric)], 2: [2 - V6 (numeric)], 3: [3 - V8 (nominal)], 4: [4 - V14 (nominal)], 5: [5 - V15 (nominal)], 6: [6 - V17 (nominal)], 7: [7 - V18 (nominal)], 8: [8 - V19 (nominal)], 9: [9 - V20 (numeric)], 10: [10 - V21 (numeric)], 11: [11 - V22 (nominal)], 12: [12 - class (no...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 13.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 8.0,...
rl_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "V1", "V5", "V6", "V8", "V14", "V15", "V17", "V18", "V19", "V20", "V21", "V22" ]
[ false, false, false, true, true, true, true, true, true, false, false, true ]
3,092
362,703
predictive_accuracy
accuracy_score
compass_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset compass (44162) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int ...
{0: [0 - sex (nominal)], 1: [1 - age (numeric)], 2: [2 - age_cat (nominal)], 3: [3 - race (nominal)], 4: [4 - juv_fel_count (numeric)], 5: [5 - juv_misd_count (numeric)], 6: [6 - juv_other_count (numeric)], 7: [7 - priors_count (numeric)], 8: [8 - days_b_screening_arrest (numeric)], 9: [9 - c_days_from_compas ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 18.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 10.0...
compass_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "sex", "age", "age_cat", "race", "juv_fel_count", "juv_misd_count", "juv_other_count", "priors_count", "days_b_screening_arrest", "c_days_from_compas", "c_charge_degree", "decile_score.1", "score_text", "v_type_of_assessment", "v_decile_score", "v_score_text", "end" ]
[ true, false, true, true, false, false, false, false, false, false, true, true, true, true, true, true, false ]
3,093
362,715
predictive_accuracy
accuracy_score
yeast_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset yeast (181) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10...
{0: [0 - mcg (numeric)], 1: [1 - gvh (numeric)], 2: [2 - alm (numeric)], 3: [3 - mit (numeric)], 4: [4 - erl (numeric)], 5: [5 - pox (numeric)], 6: [6 - vac (numeric)], 7: [7 - nuc (numeric)], 8: [8 - class_protein_localization (nominal)]}
{'MajorityClassSize': 463.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 5.0, 'NumberOfClasses': 10.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 1484.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 1.0, 'c...
yeast_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "mcg", "gvh", "alm", "mit", "erl", "pox", "vac", "nuc" ]
[ false, false, false, false, false, false, false, false ]
3,094
362,714
predictive_accuracy
accuracy_score
yeast_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset yeast (181) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10...
{0: [0 - mcg (numeric)], 1: [1 - gvh (numeric)], 2: [2 - alm (numeric)], 3: [3 - mit (numeric)], 4: [4 - erl (numeric)], 5: [5 - pox (numeric)], 6: [6 - vac (numeric)], 7: [7 - nuc (numeric)], 8: [8 - class_protein_localization (nominal)]}
{'MajorityClassSize': 463.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 5.0, 'NumberOfClasses': 10.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 1484.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 1.0, 'c...
yeast_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "mcg", "gvh", "alm", "mit", "erl", "pox", "vac", "nuc" ]
[ false, false, false, false, false, false, false, false ]
3,095
362,704
predictive_accuracy
accuracy_score
compass_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset compass (44162) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int ...
{0: [0 - sex (nominal)], 1: [1 - age (numeric)], 2: [2 - age_cat (nominal)], 3: [3 - race (nominal)], 4: [4 - juv_fel_count (numeric)], 5: [5 - juv_misd_count (numeric)], 6: [6 - juv_other_count (numeric)], 7: [7 - priors_count (numeric)], 8: [8 - days_b_screening_arrest (numeric)], 9: [9 - c_days_from_compas ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 18.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 10.0...
compass_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "sex", "age", "age_cat", "race", "juv_fel_count", "juv_misd_count", "juv_other_count", "priors_count", "days_b_screening_arrest", "c_days_from_compas", "c_charge_degree", "decile_score.1", "score_text", "v_type_of_assessment", "v_decile_score", "v_score_text", "end" ]
[ true, false, true, true, false, false, false, false, false, false, true, true, true, true, true, true, false ]
3,096
362,683
predictive_accuracy
accuracy_score
eye_movements_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
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( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - P1stFixation (nominal)], 3: [3 - P2stFixation (nominal)], 4: [4 - prevFixDur (numeric)], 5: [5 - firstfixDur (numeric)], 6: [6 - firstPassFixDur (numeric)], 7: [7 - nextFixDur (numeric)], 8: [8 - firstSaccLen (numeric)], 9: [9 - lastSaccLen (numeri...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 24.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 4.0...
eye_movements_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lineNo", "assgNo", "P1stFixation", "P2stFixation", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "nextWordRegress", "regressDur", "pupilDiamMax", ...
[ false, false, true, true, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false ]
3,097
362,706
predictive_accuracy
accuracy_score
compass_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset compass (44162) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int ...
{0: [0 - sex (nominal)], 1: [1 - age (numeric)], 2: [2 - age_cat (nominal)], 3: [3 - race (nominal)], 4: [4 - juv_fel_count (numeric)], 5: [5 - juv_misd_count (numeric)], 6: [6 - juv_other_count (numeric)], 7: [7 - priors_count (numeric)], 8: [8 - days_b_screening_arrest (numeric)], 9: [9 - c_days_from_compas ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 18.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 10.0...
compass_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "sex", "age", "age_cat", "race", "juv_fel_count", "juv_misd_count", "juv_other_count", "priors_count", "days_b_screening_arrest", "c_days_from_compas", "c_charge_degree", "decile_score.1", "score_text", "v_type_of_assessment", "v_decile_score", "v_score_text", "end" ]
[ true, false, true, true, false, false, false, false, false, false, true, true, true, true, true, true, false ]
3,098
362,379
predictive_accuracy
accuracy_score
WMO-Hurricane-Survival-Dataset
It is estimated that 10,000 people die each year worldwide due to hurricanes and tropical storms. The majority of human deaths are caused by flooding. Hurricane Irma hit Florida as a Category 4 storm the morning of Sept. 10, 2017, ripping off roofs, flooding coastal cities, and knocking out power to more than people. T...
{0: [0 - ID (numeric)], 1: [1 - DOB (string)], 2: [2 - M_STATUS (string)], 3: [3 - SALARY (string)], 4: [4 - EDU_DATA (string)], 5: [5 - EMP_DATA (string)], 6: [6 - REL_ORIEN (string)], 7: [7 - FAV_TV (string)], 8: [8 - PREF_CAR (string)], 9: [9 - GENDER (string)], 10: [10 - FAV_CUIS (string)], 11: [11 - FAV...
{'MajorityClassSize': 2584.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 2437.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 23.0, 'NumberOfInstances': 5021.0, 'NumberOfInstancesWithMissingValues': 70.0, 'NumberOfMissingValues': 91.0, 'NumberOfNumericFeatures': 1.0, 'NumberOfSymbolicFeatures': 0....
WMO-Hurricane-Survival-Dataset
[ "DOB", "M_STATUS", "SALARY", "EDU_DATA", "EMP_DATA", "REL_ORIEN", "FAV_TV", "PREF_CAR", "GENDER", "FAV_CUIS", "FAV_MUSIC", "ENDU_LEVEL", "FAV_SPORT", "FAV_COLR", "NEWS_SOURCE", "DIST_FRM_COAST", "MNTLY_TRAVEL", "GEN_MOVIES", "FAV_SUBJ", "ALCOHOL", "FAV_SUPERHERO", "Dist_Coa...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,099
362,455
predictive_accuracy
accuracy_score
eye_movements
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 numerical features" benchmark. Original description: **Author**: **Source**: Unknown - Date unknown **Please cite**: Jarkko Salojarvi, Kai Puol...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - prevFixDur (numeric)], 3: [3 - firstfixDur (numeric)], 4: [4 - firstPassFixDur (nominal)], 5: [5 - nextFixDur (nominal)], 6: [6 - firstSaccLen (numeric)], 7: [7 - lastSaccLen (numeric)], 8: [8 - prevFixPos (numeric)], 9: [9 - landingPos (numeric)],...
{'MajorityClassSize': 3804.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 3804.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 7608.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 17.0, 'NumberOfSymbolicFeatures': 3.0...
eye_movements
[ "lineNo", "assgNo", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "regressDur", "pupilDiamMax", "pupilDiamLag", "timePrtctg", "titleNo", "wordNo" ...
[ false, false, false, false, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, true ]
3,100
362,713
predictive_accuracy
accuracy_score
yeast_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset yeast (181) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10...
{0: [0 - mcg (numeric)], 1: [1 - gvh (numeric)], 2: [2 - alm (numeric)], 3: [3 - mit (numeric)], 4: [4 - erl (numeric)], 5: [5 - pox (numeric)], 6: [6 - vac (numeric)], 7: [7 - nuc (numeric)], 8: [8 - class_protein_localization (nominal)]}
{'MajorityClassSize': 463.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 5.0, 'NumberOfClasses': 10.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 1484.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 1.0, 'c...
yeast_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "mcg", "gvh", "alm", "mit", "erl", "pox", "vac", "nuc" ]
[ false, false, false, false, false, false, false, false ]
3,101
362,429
predictive_accuracy
accuracy_score
eye_movements
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 categorical and numerical features" benchmark. Original description: **Author**: **Source**: Un...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - P1stFixation (nominal)], 3: [3 - P2stFixation (nominal)], 4: [4 - prevFixDur (numeric)], 5: [5 - firstfixDur (numeric)], 6: [6 - firstPassFixDur (numeric)], 7: [7 - nextFixDur (numeric)], 8: [8 - firstSaccLen (numeric)], 9: [9 - lastSaccLen (numeri...
{'MajorityClassSize': 3804.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 3804.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 24.0, 'NumberOfInstances': 7608.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 3.0...
eye_movements
[ "lineNo", "assgNo", "P1stFixation", "P2stFixation", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "nextWordRegress", "regressDur", "pupilDiamMax", ...
[ false, false, true, true, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false ]
3,102
362,685
predictive_accuracy
accuracy_score
eye_movements_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset eye_movements (44157) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - P1stFixation (nominal)], 3: [3 - P2stFixation (nominal)], 4: [4 - prevFixDur (numeric)], 5: [5 - firstfixDur (numeric)], 6: [6 - firstPassFixDur (numeric)], 7: [7 - nextFixDur (numeric)], 8: [8 - firstSaccLen (numeric)], 9: [9 - lastSaccLen (numeri...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 24.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 4.0...
eye_movements_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lineNo", "assgNo", "P1stFixation", "P2stFixation", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "nextWordRegress", "regressDur", "pupilDiamMax", ...
[ false, false, true, true, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false ]
3,103
362,716
predictive_accuracy
accuracy_score
yeast_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset yeast (181) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10...
{0: [0 - mcg (numeric)], 1: [1 - gvh (numeric)], 2: [2 - alm (numeric)], 3: [3 - mit (numeric)], 4: [4 - erl (numeric)], 5: [5 - pox (numeric)], 6: [6 - vac (numeric)], 7: [7 - nuc (numeric)], 8: [8 - class_protein_localization (nominal)]}
{'MajorityClassSize': 463.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 5.0, 'NumberOfClasses': 10.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 1484.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 1.0, 'c...
yeast_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "mcg", "gvh", "alm", "mit", "erl", "pox", "vac", "nuc" ]
[ false, false, false, false, false, false, false, false ]
3,104
362,687
predictive_accuracy
accuracy_score
eye_movements_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset eye_movements (44157) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - P1stFixation (nominal)], 3: [3 - P2stFixation (nominal)], 4: [4 - prevFixDur (numeric)], 5: [5 - firstfixDur (numeric)], 6: [6 - firstPassFixDur (numeric)], 7: [7 - nextFixDur (numeric)], 8: [8 - firstSaccLen (numeric)], 9: [9 - lastSaccLen (numeri...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 24.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 4.0...
eye_movements_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lineNo", "assgNo", "P1stFixation", "P2stFixation", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "nextWordRegress", "regressDur", "pupilDiamMax", ...
[ false, false, true, true, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false ]
3,105
362,686
predictive_accuracy
accuracy_score
eye_movements_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset eye_movements (44157) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - P1stFixation (nominal)], 3: [3 - P2stFixation (nominal)], 4: [4 - prevFixDur (numeric)], 5: [5 - firstfixDur (numeric)], 6: [6 - firstPassFixDur (numeric)], 7: [7 - nextFixDur (numeric)], 8: [8 - firstSaccLen (numeric)], 9: [9 - lastSaccLen (numeri...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 24.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 4.0...
eye_movements_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lineNo", "assgNo", "P1stFixation", "P2stFixation", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "nextWordRegress", "regressDur", "pupilDiamMax", ...
[ false, false, true, true, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false ]
3,106
362,707
predictive_accuracy
accuracy_score
compass_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset compass (44162) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int ...
{0: [0 - sex (nominal)], 1: [1 - age (numeric)], 2: [2 - age_cat (nominal)], 3: [3 - race (nominal)], 4: [4 - juv_fel_count (numeric)], 5: [5 - juv_misd_count (numeric)], 6: [6 - juv_other_count (numeric)], 7: [7 - priors_count (numeric)], 8: [8 - days_b_screening_arrest (numeric)], 9: [9 - c_days_from_compas ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 18.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 10.0...
compass_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "sex", "age", "age_cat", "race", "juv_fel_count", "juv_misd_count", "juv_other_count", "priors_count", "days_b_screening_arrest", "c_days_from_compas", "c_charge_degree", "decile_score.1", "score_text", "v_type_of_assessment", "v_decile_score", "v_score_text", "end" ]
[ true, false, true, true, false, false, false, false, false, false, true, true, true, true, true, true, false ]
3,107
362,689
predictive_accuracy
accuracy_score
covertype_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset covertype (44159) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - Elevation (numeric)], 1: [1 - Aspect (numeric)], 2: [2 - Slope (numeric)], 3: [3 - Horizontal_Distance_To_Hydrology (numeric)], 4: [4 - Vertical_Distance_To_Hydrology (numeric)], 5: [5 - Horizontal_Distance_To_Roadways (numeric)], 6: [6 - Hillshade_9am (numeric)], 7: [7 - Hillshade_Noon (numeric)], 8: ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 55.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 45....
covertype_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Elevation", "Aspect", "Slope", "Horizontal_Distance_To_Hydrology", "Vertical_Distance_To_Hydrology", "Horizontal_Distance_To_Roadways", "Hillshade_9am", "Hillshade_Noon", "Hillshade_3pm", "Horizontal_Distance_To_Fire_Points", "Wilderness_Area1", "Wilderness_Area2", "Wilderness_Area3", "Wi...
[ false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, tr...
3,108
362,503
predictive_accuracy
accuracy_score
eye_movements
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 categorical and numerical features" benchmark. Original description: **Author**: **Source**: Un...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - P1stFixation (nominal)], 3: [3 - P2stFixation (nominal)], 4: [4 - prevFixDur (numeric)], 5: [5 - firstfixDur (numeric)], 6: [6 - firstPassFixDur (numeric)], 7: [7 - nextFixDur (numeric)], 8: [8 - firstSaccLen (numeric)], 9: [9 - lastSaccLen (numeri...
{'MajorityClassSize': 3804.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 3804.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 24.0, 'NumberOfInstances': 7608.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 3.0...
eye_movements
[ "lineNo", "assgNo", "P1stFixation", "P2stFixation", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "nextWordRegress", "regressDur", "pupilDiamMax", ...
[ false, false, true, true, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false ]
3,110
362,717
predictive_accuracy
accuracy_score
yeast_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset yeast (181) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10...
{0: [0 - mcg (numeric)], 1: [1 - gvh (numeric)], 2: [2 - alm (numeric)], 3: [3 - mit (numeric)], 4: [4 - erl (numeric)], 5: [5 - pox (numeric)], 6: [6 - vac (numeric)], 7: [7 - nuc (numeric)], 8: [8 - class_protein_localization (nominal)]}
{'MajorityClassSize': 463.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 5.0, 'NumberOfClasses': 10.0, 'NumberOfFeatures': 9.0, 'NumberOfInstances': 1484.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 1.0, 'c...
yeast_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "mcg", "gvh", "alm", "mit", "erl", "pox", "vac", "nuc" ]
[ false, false, false, false, false, false, false, false ]
3,111
362,684
predictive_accuracy
accuracy_score
eye_movements_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
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( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max...
{0: [0 - lineNo (numeric)], 1: [1 - assgNo (numeric)], 2: [2 - P1stFixation (nominal)], 3: [3 - P2stFixation (nominal)], 4: [4 - prevFixDur (numeric)], 5: [5 - firstfixDur (numeric)], 6: [6 - firstPassFixDur (numeric)], 7: [7 - nextFixDur (numeric)], 8: [8 - firstSaccLen (numeric)], 9: [9 - lastSaccLen (numeri...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 24.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 20.0, 'NumberOfSymbolicFeatures': 4.0...
eye_movements_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lineNo", "assgNo", "P1stFixation", "P2stFixation", "prevFixDur", "firstfixDur", "firstPassFixDur", "nextFixDur", "firstSaccLen", "lastSaccLen", "prevFixPos", "landingPos", "leavingPos", "totalFixDur", "meanFixDur", "regressLen", "nextWordRegress", "regressDur", "pupilDiamMax", ...
[ false, false, true, true, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, false, false, false, false ]
3,112
362,700
predictive_accuracy
accuracy_score
road-safety_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset road-safety (44161) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: ...
{0: [0 - Vehicle_Reference_df_res (numeric)], 1: [1 - Vehicle_Type (numeric)], 2: [2 - Vehicle_Manoeuvre (numeric)], 3: [3 - Vehicle_Location-Restricted_Lane (numeric)], 4: [4 - Hit_Object_in_Carriageway (numeric)], 5: [5 - Hit_Object_off_Carriageway (numeric)], 6: [6 - Was_Vehicle_Left_Hand_Drive? (nominal)], 7...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 33.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 29.0, 'NumberOfSymbolicFeatures': 4.0...
road-safety_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Vehicle_Reference_df_res", "Vehicle_Type", "Vehicle_Manoeuvre", "Vehicle_Location-Restricted_Lane", "Hit_Object_in_Carriageway", "Hit_Object_off_Carriageway", "Was_Vehicle_Left_Hand_Drive?", "Age_of_Driver", "Age_Band_of_Driver", "Engine_Capacity_(CC)", "Propulsion_Code", "Age_of_Vehicle", ...
[ false, false, false, false, false, false, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, true, false, false, false, false, false, false ]
3,113
362,428
predictive_accuracy
accuracy_score
KDDCup09_upselling
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 categorical and numerical features" benchmark. Original description: **Author**: **Source**: Un...
{0: [0 - Var6 (numeric)], 1: [1 - Var13 (numeric)], 2: [2 - Var21 (numeric)], 3: [3 - Var22 (numeric)], 4: [4 - Var24 (numeric)], 5: [5 - Var25 (numeric)], 6: [6 - Var28 (numeric)], 7: [7 - Var35 (numeric)], 8: [8 - Var38 (numeric)], 9: [9 - Var57 (numeric)], 10: [10 - Var65 (numeric)], 11: [11 - Var73 (nume...
{'MajorityClassSize': 2516.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 2516.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 46.0, 'NumberOfInstances': 5032.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 34.0, 'NumberOfSymbolicFeatures': 11....
KDDCup09_upselling
[ "Var6", "Var13", "Var21", "Var22", "Var24", "Var25", "Var28", "Var35", "Var38", "Var57", "Var65", "Var73", "Var74", "Var76", "Var78", "Var81", "Var83", "Var85", "Var109", "Var112", "Var113", "Var119", "Var123", "Var125", "Var126", "Var132", "Var133", "Var134", ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, tr...
3,114
146,580
predictive_accuracy
accuracy_score
gina_agnostic
**Author**: [Isabelle Guyon](isabelle@clopinet.com) **Source**: [Agnostic Learning vs. Prior Knowledge Challenge](http://www.agnostic.inf.ethz.ch) **Please cite**: None Dataset from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch), which consisted of 5 different datasets (SYLV...
{0: [0 - attr0 (numeric)], 1: [1 - attr1 (numeric)], 2: [2 - attr2 (numeric)], 3: [3 - attr3 (numeric)], 4: [4 - attr4 (numeric)], 5: [5 - attr5 (numeric)], 6: [6 - attr6 (numeric)], 7: [7 - attr7 (numeric)], 8: [8 - attr8 (numeric)], 9: [9 - attr9 (numeric)], 10: [10 - attr10 (numeric)], 11: [11 - attr11 (n...
{'MajorityClassSize': 1763.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 1705.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 971.0, 'NumberOfInstances': 3468.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 970.0, 'NumberOfSymbolicFeatures': 1...
gina_agnostic
[ "attr0", "attr1", "attr2", "attr3", "attr4", "attr5", "attr6", "attr7", "attr8", "attr9", "attr10", "attr11", "attr12", "attr13", "attr14", "attr15", "attr16", "attr17", "attr18", "attr19", "attr20", "attr21", "attr22", "attr23", "attr24", "attr25", "attr26", "a...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
3,115
362,705
predictive_accuracy
accuracy_score
compass_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset compass (44162) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int ...
{0: [0 - sex (nominal)], 1: [1 - age (numeric)], 2: [2 - age_cat (nominal)], 3: [3 - race (nominal)], 4: [4 - juv_fel_count (numeric)], 5: [5 - juv_misd_count (numeric)], 6: [6 - juv_other_count (numeric)], 7: [7 - priors_count (numeric)], 8: [8 - days_b_screening_arrest (numeric)], 9: [9 - c_days_from_compas ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 18.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 8.0, 'NumberOfSymbolicFeatures': 10.0...
compass_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "sex", "age", "age_cat", "race", "juv_fel_count", "juv_misd_count", "juv_other_count", "priors_count", "days_b_screening_arrest", "c_days_from_compas", "c_charge_degree", "decile_score.1", "score_text", "v_type_of_assessment", "v_decile_score", "v_score_text", "end" ]
[ true, false, true, true, false, false, false, false, false, false, true, true, true, true, true, true, false ]
3,116
362,701
predictive_accuracy
accuracy_score
road-safety_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset road-safety (44161) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: ...
{0: [0 - Vehicle_Reference_df_res (numeric)], 1: [1 - Vehicle_Type (numeric)], 2: [2 - Vehicle_Manoeuvre (numeric)], 3: [3 - Vehicle_Location-Restricted_Lane (numeric)], 4: [4 - Hit_Object_in_Carriageway (numeric)], 5: [5 - Hit_Object_off_Carriageway (numeric)], 6: [6 - Was_Vehicle_Left_Hand_Drive? (nominal)], 7...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 33.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 29.0, 'NumberOfSymbolicFeatures': 4.0...
road-safety_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Vehicle_Reference_df_res", "Vehicle_Type", "Vehicle_Manoeuvre", "Vehicle_Location-Restricted_Lane", "Hit_Object_in_Carriageway", "Hit_Object_off_Carriageway", "Was_Vehicle_Left_Hand_Drive?", "Age_of_Driver", "Age_Band_of_Driver", "Engine_Capacity_(CC)", "Propulsion_Code", "Age_of_Vehicle", ...
[ false, false, false, false, false, false, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, true, false, false, false, false, false, false ]
3,117
4,597
predictive_accuracy
accuracy_score
hiva_agnostic
**Author**: **Source**: Unknown - Date unknown **Please cite**: Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch) Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php Modified by TunedIT (converted to ARFF format) HIVA is the HIV infection database ...
{0: [0 - attr0 (numeric)], 1: [1 - attr1 (numeric)], 2: [2 - attr2 (numeric)], 3: [3 - attr3 (numeric)], 4: [4 - attr4 (numeric)], 5: [5 - attr5 (numeric)], 6: [6 - attr6 (numeric)], 7: [7 - attr7 (numeric)], 8: [8 - attr8 (numeric)], 9: [9 - attr9 (numeric)], 10: [10 - attr10 (numeric)], 11: [11 - attr11 (n...
{'MajorityClassSize': 4080.0, 'MaxNominalAttDistinctValues': 2.0, 'MinorityClassSize': 149.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 1618.0, 'NumberOfInstances': 4229.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 1617.0, 'NumberOfSymbolicFeatures': ...
hiva_agnostic
[ "attr0", "attr1", "attr2", "attr3", "attr4", "attr5", "attr6", "attr7", "attr8", "attr9", "attr10", "attr11", "attr12", "attr13", "attr14", "attr15", "attr16", "attr17", "attr18", "attr19", "attr20", "attr21", "attr22", "attr23", "attr24", "attr25", "attr26", "a...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
3,118
362,691
predictive_accuracy
accuracy_score
covertype_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset covertype (44159) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - Elevation (numeric)], 1: [1 - Aspect (numeric)], 2: [2 - Slope (numeric)], 3: [3 - Horizontal_Distance_To_Hydrology (numeric)], 4: [4 - Vertical_Distance_To_Hydrology (numeric)], 5: [5 - Horizontal_Distance_To_Roadways (numeric)], 6: [6 - Hillshade_9am (numeric)], 7: [7 - Hillshade_Noon (numeric)], 8: ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 55.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 45....
covertype_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Elevation", "Aspect", "Slope", "Horizontal_Distance_To_Hydrology", "Vertical_Distance_To_Hydrology", "Horizontal_Distance_To_Roadways", "Hillshade_9am", "Hillshade_Noon", "Hillshade_3pm", "Horizontal_Distance_To_Fire_Points", "Wilderness_Area1", "Wilderness_Area2", "Wilderness_Area3", "Wi...
[ false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, tr...
3,119
362,699
predictive_accuracy
accuracy_score
road-safety_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset road-safety (44161) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: ...
{0: [0 - Vehicle_Reference_df_res (numeric)], 1: [1 - Vehicle_Type (numeric)], 2: [2 - Vehicle_Manoeuvre (numeric)], 3: [3 - Vehicle_Location-Restricted_Lane (numeric)], 4: [4 - Hit_Object_in_Carriageway (numeric)], 5: [5 - Hit_Object_off_Carriageway (numeric)], 6: [6 - Was_Vehicle_Left_Hand_Drive? (nominal)], 7...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 33.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 29.0, 'NumberOfSymbolicFeatures': 4.0...
road-safety_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Vehicle_Reference_df_res", "Vehicle_Type", "Vehicle_Manoeuvre", "Vehicle_Location-Restricted_Lane", "Hit_Object_in_Carriageway", "Hit_Object_off_Carriageway", "Was_Vehicle_Left_Hand_Drive?", "Age_of_Driver", "Age_Band_of_Driver", "Engine_Capacity_(CC)", "Propulsion_Code", "Age_of_Vehicle", ...
[ false, false, false, false, false, false, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, true, false, false, false, false, false, false ]
3,120
362,729
predictive_accuracy
accuracy_score
Australian_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset Australian (40981) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: i...
{0: [0 - A1 (nominal)], 1: [1 - A2 (numeric)], 2: [2 - A3 (numeric)], 3: [3 - A4 (nominal)], 4: [4 - A5 (nominal)], 5: [5 - A6 (nominal)], 6: [6 - A7 (numeric)], 7: [7 - A8 (nominal)], 8: [8 - A9 (nominal)], 9: [9 - A10 (numeric)], 10: [10 - A11 (nominal)], 11: [11 - A12 (nominal)], 12: [12 - A13 (numeric)]...
{'MajorityClassSize': 383.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 307.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 15.0, 'NumberOfInstances': 690.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 6.0, 'NumberOfSymbolicFeatures': 9.0, '...
Australian_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14" ]
[ true, false, false, true, true, true, false, true, true, false, true, true, false, false ]
3,121
362,688
predictive_accuracy
accuracy_score
covertype_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset covertype (44159) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - Elevation (numeric)], 1: [1 - Aspect (numeric)], 2: [2 - Slope (numeric)], 3: [3 - Horizontal_Distance_To_Hydrology (numeric)], 4: [4 - Vertical_Distance_To_Hydrology (numeric)], 5: [5 - Horizontal_Distance_To_Roadways (numeric)], 6: [6 - Hillshade_9am (numeric)], 7: [7 - Hillshade_Noon (numeric)], 8: ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 55.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 45....
covertype_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Elevation", "Aspect", "Slope", "Horizontal_Distance_To_Hydrology", "Vertical_Distance_To_Hydrology", "Horizontal_Distance_To_Roadways", "Hillshade_9am", "Hillshade_Noon", "Hillshade_3pm", "Horizontal_Distance_To_Fire_Points", "Wilderness_Area1", "Wilderness_Area2", "Wilderness_Area3", "Wi...
[ false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, tr...
3,122
362,733
predictive_accuracy
accuracy_score
Australian_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset Australian (40981) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: i...
{0: [0 - A1 (nominal)], 1: [1 - A2 (numeric)], 2: [2 - A3 (numeric)], 3: [3 - A4 (nominal)], 4: [4 - A5 (nominal)], 5: [5 - A6 (nominal)], 6: [6 - A7 (numeric)], 7: [7 - A8 (nominal)], 8: [8 - A9 (nominal)], 9: [9 - A10 (numeric)], 10: [10 - A11 (nominal)], 11: [11 - A12 (nominal)], 12: [12 - A13 (numeric)]...
{'MajorityClassSize': 383.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 307.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 15.0, 'NumberOfInstances': 690.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 6.0, 'NumberOfSymbolicFeatures': 9.0, '...
Australian_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14" ]
[ true, false, false, true, true, true, false, true, true, false, true, true, false, false ]
3,123
362,692
predictive_accuracy
accuracy_score
covertype_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset covertype (44159) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - Elevation (numeric)], 1: [1 - Aspect (numeric)], 2: [2 - Slope (numeric)], 3: [3 - Horizontal_Distance_To_Hydrology (numeric)], 4: [4 - Vertical_Distance_To_Hydrology (numeric)], 5: [5 - Horizontal_Distance_To_Roadways (numeric)], 6: [6 - Hillshade_9am (numeric)], 7: [7 - Hillshade_Noon (numeric)], 8: ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 55.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 45....
covertype_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Elevation", "Aspect", "Slope", "Horizontal_Distance_To_Hydrology", "Vertical_Distance_To_Hydrology", "Horizontal_Distance_To_Roadways", "Hillshade_9am", "Hillshade_Noon", "Hillshade_3pm", "Horizontal_Distance_To_Fire_Points", "Wilderness_Area1", "Wilderness_Area2", "Wilderness_Area3", "Wi...
[ false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, tr...
3,124
362,698
predictive_accuracy
accuracy_score
road-safety_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset road-safety (44161) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: ...
{0: [0 - Vehicle_Reference_df_res (numeric)], 1: [1 - Vehicle_Type (numeric)], 2: [2 - Vehicle_Manoeuvre (numeric)], 3: [3 - Vehicle_Location-Restricted_Lane (numeric)], 4: [4 - Hit_Object_in_Carriageway (numeric)], 5: [5 - Hit_Object_off_Carriageway (numeric)], 6: [6 - Was_Vehicle_Left_Hand_Drive? (nominal)], 7...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 33.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 29.0, 'NumberOfSymbolicFeatures': 4.0...
road-safety_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Vehicle_Reference_df_res", "Vehicle_Type", "Vehicle_Manoeuvre", "Vehicle_Location-Restricted_Lane", "Hit_Object_in_Carriageway", "Hit_Object_off_Carriageway", "Was_Vehicle_Left_Hand_Drive?", "Age_of_Driver", "Age_Band_of_Driver", "Engine_Capacity_(CC)", "Propulsion_Code", "Age_of_Vehicle", ...
[ false, false, false, false, false, false, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, true, false, false, false, false, false, false ]
3,125
362,736
predictive_accuracy
accuracy_score
wilt_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset wilt (40983) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 1...
{0: [0 - GLCM_Pan (numeric)], 1: [1 - Mean_G (numeric)], 2: [2 - Mean_R (numeric)], 3: [3 - Mean_NIR (numeric)], 4: [4 - SD_Plan (numeric)], 5: [5 - class (nominal)]}
{'MajorityClassSize': 1892.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 108.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
wilt_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "GLCM_Pan", "Mean_G", "Mean_R", "Mean_NIR", "SD_Plan" ]
[ false, false, false, false, false ]
3,126
362,731
predictive_accuracy
accuracy_score
Australian_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset Australian (40981) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: i...
{0: [0 - A1 (nominal)], 1: [1 - A2 (numeric)], 2: [2 - A3 (numeric)], 3: [3 - A4 (nominal)], 4: [4 - A5 (nominal)], 5: [5 - A6 (nominal)], 6: [6 - A7 (numeric)], 7: [7 - A8 (nominal)], 8: [8 - A9 (nominal)], 9: [9 - A10 (numeric)], 10: [10 - A11 (nominal)], 11: [11 - A12 (nominal)], 12: [12 - A13 (numeric)]...
{'MajorityClassSize': 383.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 307.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 15.0, 'NumberOfInstances': 690.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 6.0, 'NumberOfSymbolicFeatures': 9.0, '...
Australian_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14" ]
[ true, false, false, true, true, true, false, true, true, false, true, true, false, false ]
3,127
362,737
predictive_accuracy
accuracy_score
wilt_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset wilt (40983) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 1...
{0: [0 - GLCM_Pan (numeric)], 1: [1 - Mean_G (numeric)], 2: [2 - Mean_R (numeric)], 3: [3 - Mean_NIR (numeric)], 4: [4 - SD_Plan (numeric)], 5: [5 - class (nominal)]}
{'MajorityClassSize': 1892.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 108.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
wilt_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "GLCM_Pan", "Mean_G", "Mean_R", "Mean_NIR", "SD_Plan" ]
[ false, false, false, false, false ]
3,128
362,732
predictive_accuracy
accuracy_score
wilt_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset wilt (40983) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 1...
{0: [0 - GLCM_Pan (numeric)], 1: [1 - Mean_G (numeric)], 2: [2 - Mean_R (numeric)], 3: [3 - Mean_NIR (numeric)], 4: [4 - SD_Plan (numeric)], 5: [5 - class (nominal)]}
{'MajorityClassSize': 1892.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 108.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
wilt_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "GLCM_Pan", "Mean_G", "Mean_R", "Mean_NIR", "SD_Plan" ]
[ false, false, false, false, false ]
3,129
362,730
predictive_accuracy
accuracy_score
Australian_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset Australian (40981) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: i...
{0: [0 - A1 (nominal)], 1: [1 - A2 (numeric)], 2: [2 - A3 (numeric)], 3: [3 - A4 (nominal)], 4: [4 - A5 (nominal)], 5: [5 - A6 (nominal)], 6: [6 - A7 (numeric)], 7: [7 - A8 (nominal)], 8: [8 - A9 (nominal)], 9: [9 - A10 (numeric)], 10: [10 - A11 (nominal)], 11: [11 - A12 (nominal)], 12: [12 - A13 (numeric)]...
{'MajorityClassSize': 383.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 307.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 15.0, 'NumberOfInstances': 690.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 6.0, 'NumberOfSymbolicFeatures': 9.0, '...
Australian_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14" ]
[ true, false, false, true, true, true, false, true, true, false, true, true, false, false ]
3,130
362,735
predictive_accuracy
accuracy_score
wilt_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset wilt (40983) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 1...
{0: [0 - GLCM_Pan (numeric)], 1: [1 - Mean_G (numeric)], 2: [2 - Mean_R (numeric)], 3: [3 - Mean_NIR (numeric)], 4: [4 - SD_Plan (numeric)], 5: [5 - class (nominal)]}
{'MajorityClassSize': 1892.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 108.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
wilt_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "GLCM_Pan", "Mean_G", "Mean_R", "Mean_NIR", "SD_Plan" ]
[ false, false, false, false, false ]
3,131
362,734
predictive_accuracy
accuracy_score
wilt_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset wilt (40983) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 1...
{0: [0 - GLCM_Pan (numeric)], 1: [1 - Mean_G (numeric)], 2: [2 - Mean_R (numeric)], 3: [3 - Mean_NIR (numeric)], 4: [4 - SD_Plan (numeric)], 5: [5 - class (nominal)]}
{'MajorityClassSize': 1892.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 108.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 6.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 5.0, 'NumberOfSymbolicFeatures': 1.0, ...
wilt_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "GLCM_Pan", "Mean_G", "Mean_R", "Mean_NIR", "SD_Plan" ]
[ false, false, false, false, false ]
3,132
362,504
predictive_accuracy
accuracy_score
KDDCup09_upselling
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 categorical and numerical features" benchmark. Original description: **Author**: **Source**: Un...
{0: [0 - Var6 (numeric)], 1: [1 - Var13 (numeric)], 2: [2 - Var21 (numeric)], 3: [3 - Var22 (numeric)], 4: [4 - Var24 (numeric)], 5: [5 - Var25 (numeric)], 6: [6 - Var28 (numeric)], 7: [7 - Var35 (numeric)], 8: [8 - Var38 (numeric)], 9: [9 - Var57 (numeric)], 10: [10 - Var65 (numeric)], 11: [11 - Var73 (nume...
{'MajorityClassSize': 2516.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 2516.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 46.0, 'NumberOfInstances': 5032.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 34.0, 'NumberOfSymbolicFeatures': 11....
KDDCup09_upselling
[ "Var6", "Var13", "Var21", "Var22", "Var24", "Var25", "Var28", "Var35", "Var38", "Var57", "Var65", "Var73", "Var74", "Var76", "Var78", "Var81", "Var83", "Var85", "Var109", "Var112", "Var113", "Var119", "Var123", "Var125", "Var126", "Var132", "Var133", "Var134", ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, tr...
3,133
4,634
predictive_accuracy
accuracy_score
rsctc2010_6
**Author**: **Source**: Unknown - Date unknown **Please cite**: Data from the RSCTC 2010 Discovery Challenge. Example datasets for 6 different problems of DNA microarray data analysis and classification. All datasets contain gene expression data characterized by values of 20,000 - 65,000 attributes. Samples ar...
{0: [0 - Var1 (numeric)], 1: [1 - Var2 (numeric)], 2: [2 - Var3 (numeric)], 3: [3 - Var4 (numeric)], 4: [4 - Var5 (numeric)], 5: [5 - Var6 (numeric)], 6: [6 - Var7 (numeric)], 7: [7 - Var8 (numeric)], 8: [8 - Var9 (numeric)], 9: [9 - Var10 (numeric)], 10: [10 - Var11 (numeric)], 11: [11 - Var12 (numeric)], ...
{'MajorityClassSize': 53.0, 'MaxNominalAttDistinctValues': 5.0, 'MinorityClassSize': 7.0, 'NumberOfClasses': 5.0, 'NumberOfFeatures': 59005.0, 'NumberOfInstances': 92.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 59004.0, 'NumberOfSymbolicFeatures': 1.0,...
rsctc2010_6
[ "Var1", "Var2", "Var3", "Var4", "Var5", "Var6", "Var7", "Var8", "Var9", "Var10", "Var11", "Var12", "Var13", "Var14", "Var15", "Var16", "Var17", "Var18", "Var19", "Var20", "Var21", "Var22", "Var23", "Var24", "Var25", "Var26", "Var27", "Var28", "Var29", "Var30...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
3,134
362,745
predictive_accuracy
accuracy_score
credit-g_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset credit-g (31) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = ...
{0: [0 - checking_status (nominal)], 1: [1 - duration (numeric)], 2: [2 - credit_history (nominal)], 3: [3 - purpose (nominal)], 4: [4 - credit_amount (numeric)], 5: [5 - savings_status (nominal)], 6: [6 - employment (nominal)], 7: [7 - installment_commitment (numeric)], 8: [8 - personal_status (nominal)], 9: ...
{'MajorityClassSize': 700.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 300.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 1000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 14.0, ...
credit-g_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "checking_status", "duration", "credit_history", "purpose", "credit_amount", "savings_status", "employment", "installment_commitment", "personal_status", "other_parties", "residence_since", "property_magnitude", "age", "other_payment_plans", "housing", "existing_credits", "job", "n...
[ true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true ]
3,135
362,702
predictive_accuracy
accuracy_score
road-safety_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset road-safety (44161) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: ...
{0: [0 - Vehicle_Reference_df_res (numeric)], 1: [1 - Vehicle_Type (numeric)], 2: [2 - Vehicle_Manoeuvre (numeric)], 3: [3 - Vehicle_Location-Restricted_Lane (numeric)], 4: [4 - Hit_Object_in_Carriageway (numeric)], 5: [5 - Hit_Object_off_Carriageway (numeric)], 6: [6 - Was_Vehicle_Left_Hand_Drive? (nominal)], 7...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 33.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 29.0, 'NumberOfSymbolicFeatures': 4.0...
road-safety_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Vehicle_Reference_df_res", "Vehicle_Type", "Vehicle_Manoeuvre", "Vehicle_Location-Restricted_Lane", "Hit_Object_in_Carriageway", "Hit_Object_off_Carriageway", "Was_Vehicle_Left_Hand_Drive?", "Age_of_Driver", "Age_Band_of_Driver", "Engine_Capacity_(CC)", "Propulsion_Code", "Age_of_Vehicle", ...
[ false, false, false, false, false, false, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, false, false, true, false, false, false, false, false, false ]
3,136
146,589
predictive_accuracy
accuracy_score
cnae-9
**Author**: Patrick Marques Ciarelli, Elias Oliviera **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/CNAE-9) - 2010 **Please cite**: ### Description This is a data set containing 1080 documents of free text business descriptions of Brazilian companies categorized into a subset of 9 categories. ### ...
{0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - V3 (numeric)], 3: [3 - V4 (numeric)], 4: [4 - V5 (numeric)], 5: [5 - V6 (numeric)], 6: [6 - V7 (numeric)], 7: [7 - V8 (numeric)], 8: [8 - V9 (numeric)], 9: [9 - V10 (numeric)], 10: [10 - V11 (numeric)], 11: [11 - V12 (numeric)], 12: [12 - V13 (numeric)]...
{'MajorityClassSize': 120.0, 'MaxNominalAttDistinctValues': 9.0, 'MinorityClassSize': 120.0, 'NumberOfClasses': 9.0, 'NumberOfFeatures': 857.0, 'NumberOfInstances': 1080.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 856.0, 'NumberOfSymbolicFeatures': 1.0...
cnae-9
[ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, f...
3,137
362,662
predictive_accuracy
accuracy_score
Higgs_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset Higgs (44129) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = ...
{0: [0 - lepton_pT (numeric)], 1: [1 - lepton_eta (numeric)], 2: [2 - lepton_phi (numeric)], 3: [3 - missing_energy_magnitude (numeric)], 4: [4 - missing_energy_phi (numeric)], 5: [5 - jet_1_pt (numeric)], 6: [6 - jet_1_eta (numeric)], 7: [7 - jet_1_phi (numeric)], 8: [8 - jet_2_pt (numeric)], 9: [9 - jet_2_et...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 25.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 24.0, 'NumberOfSymbolicFeatures': 1.0...
Higgs_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lepton_pT", "lepton_eta", "lepton_phi", "missing_energy_magnitude", "missing_energy_phi", "jet_1_pt", "jet_1_eta", "jet_1_phi", "jet_2_pt", "jet_2_eta", "jet_2_phi", "jet_3_pt", "jet_3_eta", "jet_3_phi", "jet_4_pt", "jet_4_eta", "jet_4_phi", "m_jj", "m_jjj", "m_lv", "m_jlv",...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,138
362,664
predictive_accuracy
accuracy_score
Higgs_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset Higgs (44129) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = ...
{0: [0 - lepton_pT (numeric)], 1: [1 - lepton_eta (numeric)], 2: [2 - lepton_phi (numeric)], 3: [3 - missing_energy_magnitude (numeric)], 4: [4 - missing_energy_phi (numeric)], 5: [5 - jet_1_pt (numeric)], 6: [6 - jet_1_eta (numeric)], 7: [7 - jet_1_phi (numeric)], 8: [8 - jet_2_pt (numeric)], 9: [9 - jet_2_et...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 25.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 24.0, 'NumberOfSymbolicFeatures': 1.0...
Higgs_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lepton_pT", "lepton_eta", "lepton_phi", "missing_energy_magnitude", "missing_energy_phi", "jet_1_pt", "jet_1_eta", "jet_1_phi", "jet_2_pt", "jet_2_eta", "jet_2_phi", "jet_3_pt", "jet_3_eta", "jet_3_phi", "jet_4_pt", "jet_4_eta", "jet_4_phi", "m_jj", "m_jjj", "m_lv", "m_jlv",...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,139
362,663
predictive_accuracy
accuracy_score
Higgs_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset Higgs (44129) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = ...
{0: [0 - lepton_pT (numeric)], 1: [1 - lepton_eta (numeric)], 2: [2 - lepton_phi (numeric)], 3: [3 - missing_energy_magnitude (numeric)], 4: [4 - missing_energy_phi (numeric)], 5: [5 - jet_1_pt (numeric)], 6: [6 - jet_1_eta (numeric)], 7: [7 - jet_1_phi (numeric)], 8: [8 - jet_2_pt (numeric)], 9: [9 - jet_2_et...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 25.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 24.0, 'NumberOfSymbolicFeatures': 1.0...
Higgs_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lepton_pT", "lepton_eta", "lepton_phi", "missing_energy_magnitude", "missing_energy_phi", "jet_1_pt", "jet_1_eta", "jet_1_phi", "jet_2_pt", "jet_2_eta", "jet_2_phi", "jet_3_pt", "jet_3_eta", "jet_3_phi", "jet_4_pt", "jet_4_eta", "jet_4_phi", "m_jj", "m_jjj", "m_lv", "m_jlv",...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,140
362,665
predictive_accuracy
accuracy_score
Higgs_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
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, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = ...
{0: [0 - lepton_pT (numeric)], 1: [1 - lepton_eta (numeric)], 2: [2 - lepton_phi (numeric)], 3: [3 - missing_energy_magnitude (numeric)], 4: [4 - missing_energy_phi (numeric)], 5: [5 - jet_1_pt (numeric)], 6: [6 - jet_1_eta (numeric)], 7: [7 - jet_1_phi (numeric)], 8: [8 - jet_2_pt (numeric)], 9: [9 - jet_2_et...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 25.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 24.0, 'NumberOfSymbolicFeatures': 1.0...
Higgs_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lepton_pT", "lepton_eta", "lepton_phi", "missing_energy_magnitude", "missing_energy_phi", "jet_1_pt", "jet_1_eta", "jet_1_phi", "jet_2_pt", "jet_2_eta", "jet_2_phi", "jet_3_pt", "jet_3_eta", "jet_3_phi", "jet_4_pt", "jet_4_eta", "jet_4_phi", "m_jj", "m_jjj", "m_lv", "m_jlv",...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,141
362,744
predictive_accuracy
accuracy_score
credit-g_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset credit-g (31) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = ...
{0: [0 - checking_status (nominal)], 1: [1 - duration (numeric)], 2: [2 - credit_history (nominal)], 3: [3 - purpose (nominal)], 4: [4 - credit_amount (numeric)], 5: [5 - savings_status (nominal)], 6: [6 - employment (nominal)], 7: [7 - installment_commitment (numeric)], 8: [8 - personal_status (nominal)], 9: ...
{'MajorityClassSize': 700.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 300.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 1000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 14.0, ...
credit-g_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "checking_status", "duration", "credit_history", "purpose", "credit_amount", "savings_status", "employment", "installment_commitment", "personal_status", "other_parties", "residence_since", "property_magnitude", "age", "other_payment_plans", "housing", "existing_credits", "job", "n...
[ true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true ]
3,142
362,708
predictive_accuracy
accuracy_score
KDDCup09_upselling_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset KDDCup09_upselling (44186) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasse...
{0: [0 - Var6 (numeric)], 1: [1 - Var13 (numeric)], 2: [2 - Var21 (numeric)], 3: [3 - Var22 (numeric)], 4: [4 - Var24 (numeric)], 5: [5 - Var25 (numeric)], 6: [6 - Var28 (numeric)], 7: [7 - Var35 (numeric)], 8: [8 - Var38 (numeric)], 9: [9 - Var57 (numeric)], 10: [10 - Var65 (numeric)], 11: [11 - Var73 (nume...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 50.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 34.0, 'NumberOfSymbolicFeatures': 16....
KDDCup09_upselling_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Var6", "Var13", "Var21", "Var22", "Var24", "Var25", "Var28", "Var35", "Var38", "Var57", "Var65", "Var73", "Var74", "Var76", "Var78", "Var81", "Var83", "Var85", "Var109", "Var112", "Var113", "Var119", "Var123", "Var125", "Var126", "Var132", "Var133", "Var134", ...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, tr...
3,143
362,666
predictive_accuracy
accuracy_score
Higgs_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset Higgs (44129) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = ...
{0: [0 - lepton_pT (numeric)], 1: [1 - lepton_eta (numeric)], 2: [2 - lepton_phi (numeric)], 3: [3 - missing_energy_magnitude (numeric)], 4: [4 - missing_energy_phi (numeric)], 5: [5 - jet_1_pt (numeric)], 6: [6 - jet_1_eta (numeric)], 7: [7 - jet_1_phi (numeric)], 8: [8 - jet_2_pt (numeric)], 9: [9 - jet_2_et...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 25.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 24.0, 'NumberOfSymbolicFeatures': 1.0...
Higgs_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lepton_pT", "lepton_eta", "lepton_phi", "missing_energy_magnitude", "missing_energy_phi", "jet_1_pt", "jet_1_eta", "jet_1_phi", "jet_2_pt", "jet_2_eta", "jet_2_phi", "jet_3_pt", "jet_3_eta", "jet_3_phi", "jet_4_pt", "jet_4_eta", "jet_4_phi", "m_jj", "m_jjj", "m_lv", "m_jlv",...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,144
362,743
predictive_accuracy
accuracy_score
credit-g_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset credit-g (31) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = ...
{0: [0 - checking_status (nominal)], 1: [1 - duration (numeric)], 2: [2 - credit_history (nominal)], 3: [3 - purpose (nominal)], 4: [4 - credit_amount (numeric)], 5: [5 - savings_status (nominal)], 6: [6 - employment (nominal)], 7: [7 - installment_commitment (numeric)], 8: [8 - personal_status (nominal)], 9: ...
{'MajorityClassSize': 700.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 300.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 21.0, 'NumberOfInstances': 1000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 7.0, 'NumberOfSymbolicFeatures': 14.0, ...
credit-g_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "checking_status", "duration", "credit_history", "purpose", "credit_amount", "savings_status", "employment", "installment_commitment", "personal_status", "other_parties", "residence_since", "property_magnitude", "age", "other_payment_plans", "housing", "existing_credits", "job", "n...
[ true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true ]
3,145
362,690
predictive_accuracy
accuracy_score
covertype_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
Subsampling of the dataset covertype (44159) 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: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: in...
{0: [0 - Elevation (numeric)], 1: [1 - Aspect (numeric)], 2: [2 - Slope (numeric)], 3: [3 - Horizontal_Distance_To_Hydrology (numeric)], 4: [4 - Vertical_Distance_To_Hydrology (numeric)], 5: [5 - Horizontal_Distance_To_Roadways (numeric)], 6: [6 - Hillshade_9am (numeric)], 7: [7 - Hillshade_Noon (numeric)], 8: ...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 55.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 10.0, 'NumberOfSymbolicFeatures': 45....
covertype_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "Elevation", "Aspect", "Slope", "Horizontal_Distance_To_Hydrology", "Vertical_Distance_To_Hydrology", "Horizontal_Distance_To_Roadways", "Hillshade_9am", "Hillshade_Noon", "Hillshade_3pm", "Horizontal_Distance_To_Fire_Points", "Wilderness_Area1", "Wilderness_Area2", "Wilderness_Area3", "Wi...
[ false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, tr...
3,146
362,667
predictive_accuracy
accuracy_score
Higgs_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
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, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = ...
{0: [0 - lepton_pT (numeric)], 1: [1 - lepton_eta (numeric)], 2: [2 - lepton_phi (numeric)], 3: [3 - missing_energy_magnitude (numeric)], 4: [4 - missing_energy_phi (numeric)], 5: [5 - jet_1_pt (numeric)], 6: [6 - jet_1_eta (numeric)], 7: [7 - jet_1_phi (numeric)], 8: [8 - jet_2_pt (numeric)], 9: [9 - jet_2_et...
{'MajorityClassSize': 1000.0, 'MaxNominalAttDistinctValues': nan, 'MinorityClassSize': 1000.0, 'NumberOfClasses': 2.0, 'NumberOfFeatures': 25.0, 'NumberOfInstances': 2000.0, 'NumberOfInstancesWithMissingValues': 0.0, 'NumberOfMissingValues': 0.0, 'NumberOfNumericFeatures': 24.0, 'NumberOfSymbolicFeatures': 1.0...
Higgs_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
[ "lepton_pT", "lepton_eta", "lepton_phi", "missing_energy_magnitude", "missing_energy_phi", "jet_1_pt", "jet_1_eta", "jet_1_phi", "jet_2_pt", "jet_2_eta", "jet_2_phi", "jet_3_pt", "jet_3_eta", "jet_3_phi", "jet_4_pt", "jet_4_eta", "jet_4_phi", "m_jj", "m_jjj", "m_lv", "m_jlv",...
[ false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false ]
3,147