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363,471 | mean_absolute_error | mean_absolute_error | munich-rent-index-1999 | Munich rent index dataset for 3082 appartments from 1999 originally from the book 'Regression Models, Methods and Applications', [doi:10.1007/978-3-662-63882-8](https://doi.org/10.1007/978-3-662-63882-8), by Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian D. Marx.
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363,244 | mean_absolute_error | mean_absolute_error | mabbob_ela_as_2d_regression_modcma | Algorithm performane prediction problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC. | {0: [0 - ela_meta.lin_simple.adj_r2 (numeric)],
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363,601 | root_mean_squared_error | root_mean_squared_error | Phenotype_202 | Detailed sequencing statistics and phenotypes of 202 individuals, for upload to OpenML we defined the variable 'Survive_time' as the target variable.
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363,243 | mean_absolute_error | mean_absolute_error | mabbob_ela_as_2d_regression_DifferentialEvolution | Algorithm performane prediction problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC. | {0: [0 - ela_meta.lin_simple.adj_r2 (numeric)],
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363,249 | mean_absolute_error | mean_absolute_error | mabbob_ela_as_5d_regression_modcma | Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC. | {0: [0 - ela_meta.lin_simple.adj_r2 (numeric)],
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363,250 | mean_absolute_error | mean_absolute_error | mabbob_ela_as_5d_regression_DifferentialEvolution | Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC. | {0: [0 - ela_meta.lin_simple.adj_r2 (numeric)],
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363,252 | mean_absolute_error | mean_absolute_error | mabbob_ela_as_5d_regression_RCobyla | Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC. | {0: [0 - ela_meta.lin_simple.adj_r2 (numeric)],
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363,251 | mean_absolute_error | mean_absolute_error | mabbob_ela_as_5d_regression_modde | Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC. | {0: [0 - ela_meta.lin_simple.adj_r2 (numeric)],
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363,605 | root_mean_squared_error | root_mean_squared_error | sleep-deprivation-and-cognitive-performance | Impact of sleep deprivation on cognition and reaction time
About Dataset
This dataset explores the effects of sleep deprivation on cognitive performance and emotional regulation, based on a 2024 study conducted in the Middle East. It includes 60 participants from diverse backgrounds, capturing data on sleep duration,... | {0: [0 - Sleep_Hours (numeric)],
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363,246 | mean_absolute_error | mean_absolute_error | mabbob_ela_as_2d_regression_RCobyla | Algorithm performane prediction problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC. | {0: [0 - ela_meta.lin_simple.adj_r2 (numeric)],
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363,418 | root_mean_squared_error | root_mean_squared_error | infrared_thermography_temperature | The Infrared Thermography Temperature Dataset contains temperatures read from various locations of inferred images about patients, with the addition of oral temperatures measured for each individual. The 33 features consist of gender, age, ethnicity, ambiant temperature, humidity, distance, and other temperature readin... | {0: [0 - Gender (string)],
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363,660 | mean_absolute_error | mean_absolute_error | QSAR_fish_toxicity | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
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For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
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363,717 | root_mean_squared_error | root_mean_squared_error | Phenotype_202 | Detailed sequencing statistics and phenotypes of 202 individuals, for upload to OpenML we defined the variable 'Survive_time' as the target variable.
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"C17",
"C18",
"C20",
"C22",
"SFA",
... | [
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f... | 1,149 |
363,652 | mean_absolute_error | mean_absolute_error | NHANES_age | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - age (numeric)],
1: [1 - RIAGENDR (nominal)],
2: [2 - PAQ605 (nominal)],
3: [3 - BMXBMI (numeric)],
4: [4 - LBXGLU (numeric)],
5: [5 - DIQ010 (nominal)],
6: [6 - LBXGLT (numeric)],
7: [7 - LBXIN (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
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'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
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'cost... | NHANES_age | [
"RIAGENDR",
"PAQ605",
"BMXBMI",
"LBXGLU",
"DIQ010",
"LBXGLT",
"LBXIN"
] | [
true,
true,
false,
false,
true,
false,
false
] | 1,150 |
363,448 | root_mean_squared_error | root_mean_squared_error | coffee_distribution_across_94_counties | Coffee Distribution Across 94 Counties
The dataset is generated from United Stares Department of Agriculture (USDA)
This dataset is generated from USDA - United States Department of Agriculture consisting of coffee production, supply, and distribution
Commodity chosen is Coffee, Green
Consists of 94 countries.
Years... | {0: [0 - Country (string)],
1: [1 - Year (numeric)],
2: [2 - Arabica Production (numeric)],
3: [3 - Bean Exports (numeric)],
4: [4 - Bean Imports (numeric)],
5: [5 - Beginning Stocks (numeric)],
6: [6 - Domestic Consumption (numeric)],
7: [7 - Ending Stocks (numeric)],
8: [8 - Exports (numeric)],
9: [9 - Impor... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
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'NumberOfClasses': 0.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 6016.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | coffee_distribution_across_94_counties | [
"Country",
"Year",
"Arabica Production",
"Bean Exports",
"Bean Imports",
"Beginning Stocks",
"Domestic Consumption",
"Ending Stocks",
"Exports",
"Imports",
"Other Production",
"Production",
"Roast & Ground Exports",
"Roast & Ground Imports",
"Robusta Production",
"Rst,Ground Dom. Consu... | [
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false,
false,
false,
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false,
false,
false,
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] | 1,151 |
363,722 | root_mean_squared_error | root_mean_squared_error | Phenotype_202 | Detailed sequencing statistics and phenotypes of 202 individuals, for upload to OpenML we defined the variable 'Survive_time' as the target variable.
Visit https://figshare.com/articles/dataset/Phenotypic_descriptives_of_202_yellow_drum_individuals/12317489/2?file=22706906 for more information
We drop all nan values f... | {0: [0 - Coverage_rate(%) (numeric)],
1: [1 - Clean_reads (numeric)],
2: [2 - Clean_bases (numeric)],
3: [3 - Mapped_reads (numeric)],
4: [4 - Mapped_bases (numeric)],
5: [5 - Mapping_rate(%) (numeric)],
6: [6 - Sequencing_depth (numeric)],
7: [7 - Effective_depth (numeric)],
8: [8 - Weight (numeric)],
9: [9 -... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 46.0,
'NumberOfInstances': 180.0,
'NumberOfInstancesWithMissingValues': 1.0,
'NumberOfMissingValues': 1.0,
'NumberOfNumericFeatures': 44.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Phenotype_202 | [
"Coverage_rate(%)",
"Clean_reads",
"Clean_bases",
"Mapped_reads",
"Mapped_bases",
"Mapping_rate(%)",
"Sequencing_depth",
"Effective_depth",
"Weight",
"Total_Length",
"Standard_Length",
"Height",
"Sex",
"Live_death",
"C14",
"C15",
"C16",
"C17",
"C18",
"C20",
"C22",
"SFA",
... | [
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f... | 1,152 |
363,718 | mean_absolute_error | mean_absolute_error | Phenotype_202 | Detailed sequencing statistics and phenotypes of 202 individuals, for upload to OpenML we defined the variable 'Survive_time' as the target variable.
Visit https://figshare.com/articles/dataset/Phenotypic_descriptives_of_202_yellow_drum_individuals/12317489/2?file=22706906 for more information
We drop all nan values f... | {0: [0 - Coverage_rate(%) (numeric)],
1: [1 - Clean_reads (numeric)],
2: [2 - Clean_bases (numeric)],
3: [3 - Mapped_reads (numeric)],
4: [4 - Mapped_bases (numeric)],
5: [5 - Mapping_rate(%) (numeric)],
6: [6 - Sequencing_depth (numeric)],
7: [7 - Effective_depth (numeric)],
8: [8 - Weight (numeric)],
9: [9 -... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 46.0,
'NumberOfInstances': 180.0,
'NumberOfInstancesWithMissingValues': 1.0,
'NumberOfMissingValues': 1.0,
'NumberOfNumericFeatures': 44.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Phenotype_202 | [
"Coverage_rate(%)",
"Clean_reads",
"Clean_bases",
"Mapped_reads",
"Mapped_bases",
"Mapping_rate(%)",
"Sequencing_depth",
"Effective_depth",
"Weight",
"Total_Length",
"Standard_Length",
"Height",
"Sex",
"Live_death",
"C14",
"C15",
"C16",
"C17",
"C18",
"C20",
"C22",
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... | [
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f... | 1,153 |
363,751 | mean_absolute_error | mean_absolute_error | LimeSoda_B.204_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - B01 (numeric)],
6: [6 - B02 (numeric)],
7: [7 - B03 (numeric)],
8: [8 - B04 (numeric)],
9: [9 - B05 (numeric)],
10: [10 - B06 (numeric)],
11: [11 - B07 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 204.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | LimeSoda_B.204_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"B01",
"B02",
"B03",
"B04",
"B05",
"B06",
"B07",
"B08",
"B8A",
"B09",
"B11",
"B12",
"NDVI",
"GNDVI"
] | [
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false,
false,
false,
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false,
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] | 1,154 |
363,431 | root_mean_squared_error | root_mean_squared_error | Violent_Crime_by_County_1975_to_2016 | Context
Crime rates vary across space and time. The reasons crimes are committed in some places but not others can be difficult to detect because of complex socio-economic factors, but policymakers still need to understand how crime rates are changing from place to place and from time to time to inform their policies.
... | {0: [0 - JURISDICTION (string)],
1: [1 - YEAR (string)],
2: [2 - POPULATION (numeric)],
3: [3 - MURDER (numeric)],
4: [4 - RAPE (numeric)],
5: [5 - ROBBERY (numeric)],
6: [6 - AGG__ASSAULT (numeric)],
7: [7 - B___E (numeric)],
8: [8 - LARCENY_THEFT (numeric)],
9: [9 - M_V_THEFT (numeric)],
10: [10 - GRAND_TOT... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 38.0,
'NumberOfInstances': 1008.0,
'NumberOfInstancesWithMissingValues': 24.0,
'NumberOfMissingValues': 312.0,
'NumberOfNumericFeatures': 36.0,
'NumberOfSymbolicFeatures': 0.0,
... | Violent_Crime_by_County_1975_to_2016 | [
"JURISDICTION",
"YEAR",
"POPULATION",
"MURDER",
"RAPE",
"ROBBERY",
"AGG__ASSAULT",
"B___E",
"LARCENY_THEFT",
"M_V_THEFT",
"GRAND_TOTAL",
"PERCENT_CHANGE",
"VIOLENT_CRIME_TOTAL",
"VIOLENT_CRIME_PERCENT",
"VIOLENT_CRIME_PERCENT_CHANGE",
"PROPERTY_CRIME_PERCENT",
"PROPERTY_CRIME_PERCENT... | [
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f... | 1,155 |
363,748 | mean_absolute_error | mean_absolute_error | project3 | Test Dataset for UCS | {0: [0 - mt_ssa (numeric)],
1: [1 - scm1_ssa (numeric)],
2: [2 - scm2_ssa (numeric)],
3: [3 - mt_pi (numeric)],
4: [4 - scm1_pi (numeric)],
5: [5 - scm2_pi (numeric)],
6: [6 - opc (numeric)],
7: [7 - mt (numeric)],
8: [8 - scm1 (numeric)],
9: [9 - scm2 (numeric)],
10: [10 - water (numeric)],
11: [11 - fa (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 437.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | project3 | [
"mt_ssa",
"scm1_ssa",
"scm2_ssa",
"mt_pi",
"scm1_pi",
"scm2_pi",
"opc",
"mt",
"scm1",
"scm2",
"water",
"fa",
"ca",
"sp",
"age"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,156 |
363,723 | root_mean_squared_error | root_mean_squared_error | Phenotype_202 | Detailed sequencing statistics and phenotypes of 202 individuals, for upload to OpenML we defined the variable 'Survive_time' as the target variable.
Visit https://figshare.com/articles/dataset/Phenotypic_descriptives_of_202_yellow_drum_individuals/12317489/2?file=22706906 for more information
We drop all nan values f... | {0: [0 - Coverage_rate (numeric)],
1: [1 - Clean_reads (numeric)],
2: [2 - Clean_bases (numeric)],
3: [3 - Mapped_reads (numeric)],
4: [4 - Mapped_bases (numeric)],
5: [5 - Mapping_rate (numeric)],
6: [6 - Sequencing_depth (numeric)],
7: [7 - Effective_depth (numeric)],
8: [8 - Weight (numeric)],
9: [9 - Total... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 46.0,
'NumberOfInstances': 178.0,
'NumberOfInstancesWithMissingValues': 1.0,
'NumberOfMissingValues': 1.0,
'NumberOfNumericFeatures': 46.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Phenotype_202 | [
"Coverage_rate",
"Clean_reads",
"Clean_bases",
"Mapped_reads",
"Mapped_bases",
"Mapping_rate",
"Sequencing_depth",
"Effective_depth",
"Weight",
"Total_Length",
"Standard_Length",
"Height",
"Sex",
"Live_death",
"C14",
"C15",
"C16",
"C17",
"C18",
"C20",
"C22",
"SFA",
"C16_1... | [
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f... | 1,157 |
363,663 | mean_absolute_error | mean_absolute_error | solar_flare | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - ModZurichClass (nominal)],
1: [1 - LargestSpotSize (nominal)],
2: [2 - SpotDist (nominal)],
3: [3 - Activity (nominal)],
4: [4 - Evolution (nominal)],
5: [5 - PrevFlareActivity (nominal)],
6: [6 - HistComplex (nominal)],
7: [7 - BecameComplex (nominal)],
8: [8 - RegionArea (nominal)],
9: [9 - LargestS... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 10.0,
'co... | solar_flare | [
"ModZurichClass",
"LargestSpotSize",
"SpotDist",
"Activity",
"Evolution",
"PrevFlareActivity",
"HistComplex",
"BecameComplex",
"RegionArea",
"LargestSpotArea"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,158 |
363,750 | mean_absolute_error | mean_absolute_error | project3_cat_new | Tailings based Cement Mixtures UCS | {0: [0 - mt_ssa (numeric)],
1: [1 - scm1_ssa (numeric)],
2: [2 - scm2_ssa (numeric)],
3: [3 - mt_pi (numeric)],
4: [4 - scm1_pi (numeric)],
5: [5 - scm2_pi (numeric)],
6: [6 - Type 1 (numeric)],
7: [7 - Type 2 (numeric)],
8: [8 - opc (numeric)],
9: [9 - mt (numeric)],
10: [10 - scm1 (numeric)],
11: [11 - scm... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 18.0,
'NumberOfInstances': 437.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | project3_cat_new | [
"mt_ssa",
"scm1_ssa",
"scm2_ssa",
"mt_pi",
"scm1_pi",
"scm2_pi",
"Type 1",
"Type 2",
"opc",
"mt",
"scm1",
"scm2",
"water",
"fa",
"ca",
"sp",
"age"
] | [
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,159 |
363,747 | mean_absolute_error | mean_absolute_error | ucs_scm_3 | Tailings based Cement Mixtures Project 3 | {0: [0 - mt_ssa (numeric)],
1: [1 - scm1_ssa (numeric)],
2: [2 - scm2_ssa (numeric)],
3: [3 - mt_pi (numeric)],
4: [4 - scm1_pi (numeric)],
5: [5 - scm2_pi (numeric)],
6: [6 - opc (numeric)],
7: [7 - mt (numeric)],
8: [8 - scm1 (numeric)],
9: [9 - scm2 (numeric)],
10: [10 - water (numeric)],
11: [11 - fa (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 437.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | ucs_scm_3 | [
"mt_ssa",
"scm1_ssa",
"scm2_ssa",
"mt_pi",
"scm1_pi",
"scm2_pi",
"opc",
"mt",
"scm1",
"scm2",
"water",
"fa",
"ca",
"sp",
"age"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,160 |
363,752 | mean_absolute_error | mean_absolute_error | LimeSoda_BB.250_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)],
6: [6 - G_Total_Counts (numeric)],
7: [7 - pH_ISE (numeric)],
8: [8 - B02 (numeric)],
9: [9 - B03 (numeric)],
10: [10 - B04 (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | LimeSoda_BB.250_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"ERa",
"G_Total_Counts",
"pH_ISE",
"B02",
"B03",
"B04",
"B05",
"B06",
"B07",
"B08",
"B8A",
"B11",
"B12",
"NDVI",
"GNDVI"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,161 |
363,746 | mean_absolute_error | mean_absolute_error | ucs_scm_mt | Tailings based Cement Mixtures | {0: [0 - mt_ssa (numeric)],
1: [1 - scm1_ssa (numeric)],
2: [2 - scm2_ssa (numeric)],
3: [3 - mt_pi (numeric)],
4: [4 - scm1_pi (numeric)],
5: [5 - scm2_pi (numeric)],
6: [6 - opc (numeric)],
7: [7 - mt (numeric)],
8: [8 - scm1 (numeric)],
9: [9 - scm2 (numeric)],
10: [10 - water (numeric)],
11: [11 - fa (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 437.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | ucs_scm_mt | [
"mt_ssa",
"scm1_ssa",
"scm2_ssa",
"mt_pi",
"scm1_pi",
"scm2_pi",
"opc",
"mt",
"scm1",
"scm2",
"water",
"fa",
"ca",
"sp",
"age"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,162 |
363,753 | mean_absolute_error | mean_absolute_error | LimeSoda_BB.30_1_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)],
6: [6 - pH_ISE (numeric)],
7: [7 - NDVI (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 30.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | LimeSoda_BB.30_1_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"ERa",
"pH_ISE",
"NDVI"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,163 |
363,726 | root_mean_squared_error | root_mean_squared_error | QSAR_Bioconcentration_regression | the QSAR Bioconcentration Classes Dataset is a well-known dataset used in cheminformatics and environmental chemistry. It is available from the UCI Machine Learning Repository and is often used for classification and regression tasks related to predicting the bioconcentration factor (BCF) of chemical compounds.
Datase... | {0: [0 - cas (string)],
1: [1 - smiles (string)],
2: [2 - set (string)],
3: [3 - nhm (numeric)],
4: [4 - pipc09 (numeric)],
5: [5 - pcd (numeric)],
6: [6 - x2av (numeric)],
7: [7 - mlogp (numeric)],
8: [8 - on1v (numeric)],
9: [9 - n_072 (numeric)],
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'cos... | QSAR_Bioconcentration_regression | [
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] | 1,164 |
363,749 | mean_absolute_error | mean_absolute_error | project3_cat | Tailings based Cement Mixtures Categorical | {0: [0 - mt_ssa (numeric)],
1: [1 - scm1_ssa (numeric)],
2: [2 - scm2_ssa (numeric)],
3: [3 - mt_pi (numeric)],
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8: [8 - opc (numeric)],
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11: [11 - scm2 ... | {'MajorityClassSize': nan,
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'NumberOfInstancesWithMissingValues': 266.0,
'NumberOfMissingValues': 307.0,
'NumberOfNumericFeatures': 16.0,
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... | project3_cat | [
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false,
false,
false,
false,
false
] | 1,165 |
363,443 | root_mean_squared_error | root_mean_squared_error | sustainable_development_report_zero_hunger | About Dataset
Context
The Sustainable Development Report (SDR) reviews progress made each year on the Sustainable Development Goals since their adoption by the 193 UN Member States in 2015. At the halfway mark to 2030, the Sustainable Development Report 2023 takes stock of progress made and discusses priorities to rest... | {0: [0 - country_code (string)],
1: [1 - country (string)],
2: [2 - year (numeric)],
3: [3 - sdg_index_score (numeric)],
4: [4 - goal_1_score (numeric)],
5: [5 - goal_2_score (numeric)],
6: [6 - goal_3_score (numeric)],
7: [7 - goal_4_score (numeric)],
8: [8 - goal_5_score (numeric)],
9: [9 - goal_6_score (num... | {'MajorityClassSize': nan,
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'co... | sustainable_development_report_zero_hunger | [
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"goal_1_score",
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] | 1,166 |
363,756 | mean_absolute_error | mean_absolute_error | LimeSoda_BB.30_1_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)],
6: [6 - pH_ISE (numeric)],
7: [7 - NDVI (numeric)]} | {'MajorityClassSize': nan,
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'NumberOfFeatures': 8.0,
'NumberOfInstances': 30.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | LimeSoda_BB.30_1_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"ERa",
"pH_ISE",
"NDVI"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,167 |
363,758 | mean_absolute_error | mean_absolute_error | LimeSoda_BB.51_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)],
6: [6 - pH_ISE (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 51.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | LimeSoda_BB.51_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"ERa",
"pH_ISE"
] | [
false,
false,
false,
false,
false,
false
] | 1,168 |
363,755 | mean_absolute_error | mean_absolute_error | LimeSoda_BB.250_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)],
6: [6 - G_Total_Counts (numeric)],
7: [7 - pH_ISE (numeric)],
8: [8 - B02 (numeric)],
9: [9 - B03 (numeric)],
10: [10 - B04 (numeric)],
... | {'MajorityClassSize': nan,
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'NumberOfFeatures': 20.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | LimeSoda_BB.250_dataset | [
"pH_target",
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"ERa",
"G_Total_Counts",
"pH_ISE",
"B02",
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"B07",
"B08",
"B8A",
"B11",
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"NDVI",
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] | [
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] | 1,169 |
363,670 | mean_absolute_error | mean_absolute_error | wine_quality | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - fixed_acidity (numeric)],
1: [1 - volatile_acidity (numeric)],
2: [2 - citric_acid (numeric)],
3: [3 - residual_sugar (numeric)],
4: [4 - chlorides (numeric)],
5: [5 - free_sulfur_dioxide (numeric)],
6: [6 - total_sulfur_dioxide (numeric)],
7: [7 - density (numeric)],
8: [8 - pH (numeric)],
9: [9 - su... | {'MajorityClassSize': nan,
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'NumberOfInstances': 6497.0,
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'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | wine_quality | [
"fixed_acidity",
"volatile_acidity",
"citric_acid",
"residual_sugar",
"chlorides",
"free_sulfur_dioxide",
"total_sulfur_dioxide",
"density",
"pH",
"sulphates",
"alcohol",
"wine_color"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 1,170 |
363,757 | mean_absolute_error | mean_absolute_error | LimeSoda_BB.30_2_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)],
6: [6 - G_Total_Counts (numeric)],
7: [7 - G_K (numeric)],
8: [8 - G_U (numeric)],
9: [9 - G_Th (numeric)],
10: [10 - G_Cs (numeric)],
1... | {'MajorityClassSize': nan,
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'NumberOfFeatures': 13.0,
'NumberOfInstances': 30.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | LimeSoda_BB.30_2_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"ERa",
"G_Total_Counts",
"G_K",
"G_U",
"G_Th",
"G_Cs",
"B04",
"NDVI"
] | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,171 |
363,754 | mean_absolute_error | mean_absolute_error | LimeSoda_B.204_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - B01 (numeric)],
6: [6 - B02 (numeric)],
7: [7 - B03 (numeric)],
8: [8 - B04 (numeric)],
9: [9 - B05 (numeric)],
10: [10 - B06 (numeric)],
11: [11 - B07 ... | {'MajorityClassSize': nan,
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'NumberOfClasses': 0.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 204.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | LimeSoda_B.204_dataset | [
"pH_target",
"Clay_target",
"Altitude",
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"B01",
"B02",
"B03",
"B04",
"B05",
"B06",
"B07",
"B08",
"B8A",
"B09",
"B11",
"B12",
"NDVI",
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] | 1,172 |
363,764 | mean_absolute_error | mean_absolute_error | LimeSoda_MG.112_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)],
6: [6 - B01 (numeric)],
7: [7 - B02 (numeric)],
8: [8 - B03 (numeric)],
9: [9 - B04 (numeric)],
10: [10 - B05 (numeric)],
11: [11 - B06 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 112.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | LimeSoda_MG.112_dataset | [
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] | [
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false,
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false,
false,
false,
false,
false
] | 1,173 |
363,759 | mean_absolute_error | mean_absolute_error | LimeSoda_BB.72_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)],
6: [6 - G_Total_Counts (numeric)],
7: [7 - pH_ISE (numeric)],
8: [8 - B02 (numeric)],
9: [9 - B03 (numeric)],
10: [10 - B04 (numeric)],
... | {'MajorityClassSize': nan,
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'NumberOfClasses': 0.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 72.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | LimeSoda_BB.72_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"ERa",
"G_Total_Counts",
"pH_ISE",
"B02",
"B03",
"B04",
"B05",
"B06",
"B07",
"B08",
"B8A",
"B11",
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] | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,174 |
363,766 | mean_absolute_error | mean_absolute_error | LimeSoda_MGS.101_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - B01 (numeric)],
6: [6 - B02 (numeric)],
7: [7 - B03 (numeric)],
8: [8 - B04 (numeric)],
9: [9 - B05 (numeric)],
10: [10 - B06 (numeric)],
11: [11 - B07 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 101.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | LimeSoda_MGS.101_dataset | [
"pH_target",
"Clay_target",
"Altitude",
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"B01",
"B02",
"B03",
"B04",
"B05",
"B06",
"B07",
"B08",
"B8A",
"B09",
"B11",
"B12",
"NDVI",
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] | [
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false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,175 |
363,761 | mean_absolute_error | mean_absolute_error | LimeSoda_G.104_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - B01 (numeric)],
6: [6 - B02 (numeric)],
7: [7 - B03 (numeric)],
8: [8 - B04 (numeric)],
9: [9 - B05 (numeric)],
10: [10 - B06 (numeric)],
11: [11 - B07 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 104.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | LimeSoda_G.104_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"B01",
"B02",
"B03",
"B04",
"B05",
"B06",
"B07",
"B08",
"B8A",
"B09",
"B11",
"B12",
"NDVI",
"GNDVI"
] | [
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false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,176 |
363,762 | mean_absolute_error | mean_absolute_error | LimeSoda_G.150_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)],
6: [6 - B01 (numeric)],
7: [7 - B02 (numeric)],
8: [8 - B03 (numeric)],
9: [9 - B04 (numeric)],
10: [10 - B05 (numeric)],
11: [11 - B06 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 150.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | LimeSoda_G.150_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"ERa",
"B01",
"B02",
"B03",
"B04",
"B05",
"B06",
"B07",
"B08",
"B8A",
"B09",
"B11",
"B12",
"NDVI",
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false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,177 |
363,767 | mean_absolute_error | mean_absolute_error | LimeSoda_MWP.36_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - B02 (numeric)],
6: [6 - B8A (numeric)],
7: [7 - B11 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 36.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | LimeSoda_MWP.36_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"B02",
"B8A",
"B11"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,178 |
363,771 | mean_absolute_error | mean_absolute_error | LimeSoda_NSW.52_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - B02 (numeric)],
6: [6 - B8A (numeric)],
7: [7 - B11 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 52.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | LimeSoda_NSW.52_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"B02",
"B8A",
"B11"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,179 |
363,765 | mean_absolute_error | mean_absolute_error | LimeSoda_MG.44_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - wl_431.6 (numeric)],
4: [4 - wl_437.4 (numeric)],
5: [5 - wl_443.3 (numeric)],
6: [6 - wl_449.1 (numeric)],
7: [7 - wl_454.9 (numeric)],
8: [8 - wl_460.7 (numeric)],
9: [9 - wl_466.5 (numeric)],
10: [10 - wl_... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 354.0,
'NumberOfInstances': 44.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 354.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | LimeSoda_MG.44_dataset | [
"pH_target",
"Clay_target",
"wl_431.6",
"wl_437.4",
"wl_443.3",
"wl_449.1",
"wl_454.9",
"wl_460.7",
"wl_466.5",
"wl_472.3",
"wl_478.1",
"wl_483.8",
"wl_489.6",
"wl_495.3",
"wl_501",
"wl_506.8",
"wl_512.5",
"wl_518.2",
"wl_523.8",
"wl_529.5",
"wl_535.2",
"wl_540.8",
"wl_54... | [
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... | 1,180 |
363,773 | mean_absolute_error | mean_absolute_error | LimeSoda_PC.45_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - ERa_EM (numeric)],
4: [4 - ERa_ERS (numeric)],
5: [5 - ERa_P (numeric)],
6: [6 - CSMoisture (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 45.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | LimeSoda_PC.45_dataset | [
"pH_target",
"Clay_target",
"ERa_EM",
"ERa_ERS",
"ERa_P",
"CSMoisture"
] | [
false,
false,
false,
false,
false,
false
] | 1,181 |
363,776 | mean_absolute_error | mean_absolute_error | LimeSoda_SC.50_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | LimeSoda_SC.50_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"ERa"
] | [
false,
false,
false,
false,
false
] | 1,182 |
363,727 | root_mean_squared_error | root_mean_squared_error | coffee_distribution_across_94_counties | Coffee Distribution Across 94 Counties
The dataset is generated from United Stares Department of Agriculture (USDA)
This dataset is generated from USDA - United States Department of Agriculture consisting of coffee production, supply, and distribution
Commodity chosen is Coffee, Green
Consists of 94 countries.
Years... | {0: [0 - country (string)],
1: [1 - year (numeric)],
2: [2 - arabica_production (numeric)],
3: [3 - bean_exports (numeric)],
4: [4 - bean_imports (numeric)],
5: [5 - beginning_stocks (numeric)],
6: [6 - domestic_consumption (numeric)],
7: [7 - ending_stocks (numeric)],
8: [8 - exports (numeric)],
9: [9 - impor... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 6016.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | coffee_distribution_across_94_counties | [
"country",
"year",
"arabica_production",
"bean_exports",
"bean_imports",
"beginning_stocks",
"domestic_consumption",
"ending_stocks",
"exports",
"imports",
"other_production",
"production",
"roast_ground_exports",
"roast_ground_imports",
"robusta_production",
"rst_ground_dom_consum",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,183 |
362,402 | mean_absolute_error | mean_absolute_error | communities-and-crime-binary | Communities and Crime (Binarized)
The following changes were introduced to OpenML Dataset 315.
* binarized 'racepctblack' at 0.06
binarized 'ViolentCrimesPerPop' at 0.2 | {0: [0 - V1 (numeric)],
1: [1 - state (numeric)],
2: [2 - county (numeric)],
3: [3 - community (numeric)],
4: [4 - communityname (string)],
5: [5 - fold (numeric)],
6: [6 - population (numeric)],
7: [7 - householdsize (numeric)],
8: [8 - racePctWhite (numeric)],
9: [9 - racePctAsian (numeric)],
10: [10 - race... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 129.0,
'NumberOfInstances': 1994.0,
'NumberOfInstancesWithMissingValues': 1871.0,
'NumberOfMissingValues': 39202.0,
'NumberOfNumericFeatures': 128.0,
'NumberOfSymbolicFeatures': ... | communities-and-crime-binary | [
"V1",
"state",
"county",
"community",
"communityname",
"fold",
"population",
"householdsize",
"racePctWhite",
"racePctAsian",
"racePctHisp",
"agePct12t21",
"agePct12t29",
"agePct16t24",
"agePct65up",
"numbUrban",
"pctUrban",
"medIncome",
"pctWWage",
"pctWFarmSelf",
"pctWInvIn... | [
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... | 1,184 |
363,786 | r2 | r2_score | fictif_bkd | Dataset fictif pour test OpenML | {0: [0 - feature1 (numeric)],
1: [1 - feature2 (numeric)],
2: [2 - category (string)],
3: [3 - target (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 5.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_ma... | fictif_bkd | [
"feature1",
"feature2",
"category"
] | [
false,
false,
false
] | 1,185 |
363,783 | mean_absolute_error | mean_absolute_error | LimeSoda_W.50_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)],
6: [6 - NDVI (numeric)],
7: [7 - GNDVI (numeric)],
8: [8 - XRF_Mg (numeric)],
9: [9 - XRF_Al (numeric)],
10: [10 - XRF_Si (numeric)],
11... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 18.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | LimeSoda_W.50_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"ERa",
"NDVI",
"GNDVI",
"XRF_Mg",
"XRF_Al",
"XRF_Si",
"XRF_Ca",
"XRF_Ti",
"XRF_Mn",
"XRF_Fe",
"XRF_Zn",
"XRF_Sr",
"XRF_Zr"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,186 |
363,784 | mean_absolute_error | mean_absolute_error | LimeSoda_SM.40_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - Altitude (numeric)],
4: [4 - Slope (numeric)],
5: [5 - ERa (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 40.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | LimeSoda_SM.40_dataset | [
"pH_target",
"Clay_target",
"Altitude",
"Slope",
"ERa"
] | [
false,
false,
false,
false,
false
] | 1,187 |
363,785 | mean_absolute_error | mean_absolute_error | fictif_bkd | Dataset fictif pour test OpenML | {0: [0 - feature1 (numeric)],
1: [1 - feature2 (numeric)],
2: [2 - category (string)],
3: [3 - target (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 5.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_ma... | fictif_bkd | [
"feature1",
"feature2",
"category"
] | [
false,
false,
false
] | 1,188 |
363,789 | mean_absolute_error | mean_absolute_error | fictif_bkd | Dataset fictif pour test OpenML | {0: [0 - feature1 (numeric)],
1: [1 - feature2 (numeric)],
2: [2 - category (string)],
3: [3 - target (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 20.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | fictif_bkd | [
"feature1",
"feature2",
"category"
] | [
false,
false,
false
] | 1,189 |
363,790 | mean_absolute_error | mean_absolute_error | fictif20bkdkmcven7nov2025 | Dataset fictif20bkdkmcven7nov2025 pour test OpenML | {0: [0 - feature1 (numeric)],
1: [1 - feature2 (numeric)],
2: [2 - category (string)],
3: [3 - target (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 20.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | fictif20bkdkmcven7nov2025 | [
"feature1",
"feature2",
"category"
] | [
false,
false,
false
] | 1,190 |
363,794 | mean_absolute_error | mean_absolute_error | bkd1kmc1Cavum | Dataset bkd1kmc1Cavum pour test OpenML | {0: [0 - ID (string)],
1: [1 - Age (numeric)],
2: [2 - Sexe (numeric)],
3: [3 - Tumeur_T (numeric)],
4: [4 - N_Stage (numeric)],
5: [5 - M_Stage (numeric)],
6: [6 - Symptomes (numeric)],
7: [7 - Risque (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 5.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_ma... | bkd1kmc1Cavum | [
"Age",
"Sexe",
"Tumeur_T",
"N_Stage",
"M_Stage",
"Symptomes"
] | [
false,
false,
false,
false,
false,
false
] | 1,191 |
363,801 | mean_absolute_error | mean_absolute_error | dummy_edit_test_dataset | Dataset for testing edit_dataset() function. | {0: [0 - x (numeric)], 1: [1 - y (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 3.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_ma... | dummy_edit_test_dataset | [
"x"
] | [
false
] | 1,192 |
4 | predictive_accuracy | accuracy_score | labor | **Author**: Unknown
**Source**: Collective Barganing Review, Labour Canada
**Please cite**: https://archive.ics.uci.edu/ml/citation_policy.html
Date: Tue, 15 Nov 88 15:44:08 EST
From: stan <stan@csi2.UofO.EDU>
To: aha@ICS.UCI.EDU
1. Title: Final settlements in labor negotitions in Canadian industry
2. Source I... | {0: [0 - duration (numeric)],
1: [1 - wage-increase-first-year (numeric)],
2: [2 - wage-increase-second-year (numeric)],
3: [3 - wage-increase-third-year (numeric)],
4: [4 - cost-of-living-adjustment (nominal)],
5: [5 - working-hours (numeric)],
6: [6 - pension (nominal)],
7: [7 - standby-pay (numeric)],
8: [8 ... | {'MajorityClassSize': 37.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 57.0,
'NumberOfInstancesWithMissingValues': 56.0,
'NumberOfMissingValues': 326.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 9.0,
'... | labor | [
"duration",
"wage-increase-first-year",
"wage-increase-second-year",
"wage-increase-third-year",
"cost-of-living-adjustment",
"working-hours",
"pension",
"standby-pay",
"shift-differential",
"education-allowance",
"statutory-holidays",
"vacation",
"longterm-disability-assistance",
"contrib... | [
false,
false,
false,
false,
true,
false,
true,
false,
false,
true,
false,
true,
true,
true,
true,
true
] | 1,193 |
363,800 | mean_absolute_error | mean_absolute_error | dummy_edit_test_dataset | Dataset for testing edit_dataset() function. | {0: [0 - x (numeric)], 1: [1 - y (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 3.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_ma... | dummy_edit_test_dataset | [
"x"
] | [
false
] | 1,194 |
363,796 | mean_absolute_error | mean_absolute_error | UHPC_cluster_idx | UHPC_cluster_idx_del_space | {0: [0 - index (numeric)],
1: [1 - CaO/Al2O3 (numeric)],
2: [2 - W/B (numeric)],
3: [3 - C/B (numeric)],
4: [4 - FA/B (numeric)],
5: [5 - GGBFS/B (numeric)],
6: [6 - SPowder/B (numeric)],
7: [7 - SFume/B (numeric)],
8: [8 - S/B (numeric)],
9: [9 - CA/B (numeric)],
10: [10 - SF/B (numeric)],
11: [11 - OF/B (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 412.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | UHPC_cluster_idx | [
"index",
"CaO/Al2O3",
"W/B",
"C/B",
"FA/B",
"GGBFS/B",
"SPowder/B",
"SFume/B",
"S/B",
"CA/B",
"SF/B",
"OF/B",
"Sand_type",
"AR_SF",
"Other_Fiber_Type",
"AR_OF",
"Heating_Ramp",
"Temperature",
"Age",
"Steam_Curing",
"SpecimenShape",
"UCS"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,195 |
363,781 | mean_absolute_error | mean_absolute_error | LimeSoda_SSP.58_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - wl_431.6 (numeric)],
4: [4 - wl_437.4 (numeric)],
5: [5 - wl_443.3 (numeric)],
6: [6 - wl_449.1 (numeric)],
7: [7 - wl_454.9 (numeric)],
8: [8 - wl_460.7 (numeric)],
9: [9 - wl_466.5 (numeric)],
10: [10 - wl_... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 354.0,
'NumberOfInstances': 58.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 354.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | LimeSoda_SSP.58_dataset | [
"pH_target",
"Clay_target",
"wl_431.6",
"wl_437.4",
"wl_443.3",
"wl_449.1",
"wl_454.9",
"wl_460.7",
"wl_466.5",
"wl_472.3",
"wl_478.1",
"wl_483.8",
"wl_489.6",
"wl_495.3",
"wl_501",
"wl_506.8",
"wl_512.5",
"wl_518.2",
"wl_523.8",
"wl_529.5",
"wl_535.2",
"wl_540.8",
"wl_54... | [
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... | 1,196 |
363,797 | mean_absolute_error | mean_absolute_error | UHPC-tmp1 | UHPC-tmp1 | {0: [0 - index (numeric)],
1: [1 - CaO/Al2O3 (numeric)],
2: [2 - W/B (numeric)],
3: [3 - C/B (numeric)],
4: [4 - FA/B (numeric)],
5: [5 - GGBFS/B (numeric)],
6: [6 - SPowder/B (numeric)],
7: [7 - SFume/B (numeric)],
8: [8 - S/B (numeric)],
9: [9 - CA/B (numeric)],
10: [10 - SF/B (numeric)],
11: [11 - OF/B (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 412.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | UHPC-tmp1 | [
"index",
"CaO/Al2O3",
"W/B",
"C/B",
"FA/B",
"GGBFS/B",
"SPowder/B",
"SFume/B",
"S/B",
"CA/B",
"SF/B",
"OF/B",
"Sand_type",
"AR_SF",
"Other_Fiber_Type",
"AR_OF",
"Heating_Ramp",
"Temperature",
"Age",
"Steam_Curing",
"SpecimenShape"
] | [
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false,
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false,
false,
false,
false,
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] | 1,197 |
363,795 | mean_absolute_error | mean_absolute_error | UHPC-tmp | UHPC-tmp | {0: [0 - Index (numeric)],
1: [1 - CaO/Al2O3 (numeric)],
2: [2 - W/B (numeric)],
3: [3 - C/B (numeric)],
4: [4 - FA/B (numeric)],
5: [5 - GGBFS/B (numeric)],
6: [6 - SPowder/B (numeric)],
7: [7 - SFume/B (numeric)],
8: [8 - S/B (numeric)],
9: [9 - CA/B (numeric)],
10: [10 - SF/B (numeric)],
11: [11 - OF/B (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 29.0,
'NumberOfInstances': 412.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 11.0,
'co... | UHPC-tmp | [
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"CaO/Al2O3",
"W/B",
"C/B",
"FA/B",
"GGBFS/B",
"SPowder/B",
"SFume/B",
"S/B",
"CA/B",
"SF/B",
"OF/B",
"AR_SF",
"AR_OF",
"Heating_Ramp",
"Temperature",
"Age",
"Sand_type_Quartz",
"Sand_type_Silica",
"Other_Fiber_Type_Basalt",
"Other_Fiber_Type_No",
"Other_Fiber_Type_... | [
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,198 |
363,779 | mean_absolute_error | mean_absolute_error | LimeSoda_SP.231_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOM_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - wl_350 (numeric)],
4: [4 - wl_355 (numeric)],
5: [5 - wl_360 (numeric)],
6: [6 - wl_365 (numeric)],
7: [7 - wl_370 (numeric)],
8: [8 - wl_375 (numeric)],
9: [9 - wl_380 (numeric)],
10: [10 - wl_385 (numeric)]... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 274.0,
'NumberOfInstances': 231.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 274.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | LimeSoda_SP.231_dataset | [
"pH_target",
"Clay_target",
"wl_350",
"wl_355",
"wl_360",
"wl_365",
"wl_370",
"wl_375",
"wl_380",
"wl_385",
"wl_390",
"wl_395",
"wl_400",
"wl_405",
"wl_410",
"wl_415",
"wl_420",
"wl_425",
"wl_430",
"wl_435",
"wl_440",
"wl_445",
"wl_450",
"wl_455",
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f... | 1,199 |
10 | predictive_accuracy | accuracy_score | lymph | **Author**:
**Source**: Unknown -
**Please cite**:
Citation Request:
This lymphography domain was obtained from the University Medical Centre,
Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and
M. Soklic for providing the data. Please include this citation if you plan
... | {0: [0 - lymphatics (nominal)],
1: [1 - block_of_affere (nominal)],
2: [2 - bl_of_lymph_c (nominal)],
3: [3 - bl_of_lymph_s (nominal)],
4: [4 - by_pass (nominal)],
5: [5 - extravasates (nominal)],
6: [6 - regeneration_of (nominal)],
7: [7 - early_uptake_in (nominal)],
8: [8 - lym_nodes_dimin (numeric)],
9: [9 ... | {'MajorityClassSize': 81.0,
'MaxNominalAttDistinctValues': 8.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 148.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 16.0,
'co... | lymph | [
"lymphatics",
"block_of_affere",
"bl_of_lymph_c",
"bl_of_lymph_s",
"by_pass",
"extravasates",
"regeneration_of",
"early_uptake_in",
"lym_nodes_dimin",
"lym_nodes_enlar",
"changes_in_lym",
"defect_in_node",
"changes_in_node",
"changes_in_stru",
"special_forms",
"dislocation_of",
"excl... | [
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] | 1,200 |
9 | predictive_accuracy | accuracy_score | autos | **Author**: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Automobile) - 1987
**Please cite**:
**1985 Auto Imports Database**
This data set consists of three types of entities: (a) the specification of an auto in terms of various characteris... | {0: [0 - normalized-losses (numeric)],
1: [1 - make (nominal)],
2: [2 - fuel-type (nominal)],
3: [3 - aspiration (nominal)],
4: [4 - num-of-doors (nominal)],
5: [5 - body-style (nominal)],
6: [6 - drive-wheels (nominal)],
7: [7 - engine-location (nominal)],
8: [8 - wheel-base (numeric)],
9: [9 - length (numeri... | {'MajorityClassSize': 67.0,
'MaxNominalAttDistinctValues': 22.0,
'MinorityClassSize': 3.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 205.0,
'NumberOfInstancesWithMissingValues': 46.0,
'NumberOfMissingValues': 59.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 11.0,
... | autos | [
"normalized-losses",
"make",
"fuel-type",
"aspiration",
"num-of-doors",
"body-style",
"drive-wheels",
"engine-location",
"wheel-base",
"length",
"width",
"height",
"curb-weight",
"engine-type",
"num-of-cylinders",
"engine-size",
"fuel-system",
"bore",
"stroke",
"compression-rat... | [
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true,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,201 |
363,039 | mean_absolute_error | mean_absolute_error | ECG5000 | The original dataset for 'ECG5000' is a 20-hour long ECG downloaded from Physionet. The name is BIDMC Congestive Heart Failure Database(chfdb) and it is record 'chf07'. It was originally published in 'Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. Physi... | {0: [0 - col_0 (numeric)],
1: [1 - col_1 (numeric)],
2: [2 - col_2 (numeric)],
3: [3 - col_3 (numeric)],
4: [4 - col_4 (numeric)],
5: [5 - col_5 (numeric)],
6: [6 - col_6 (numeric)],
7: [7 - col_7 (numeric)],
8: [8 - col_8 (numeric)],
9: [9 - col_9 (numeric)],
10: [10 - col_10 (numeric)],
11: [11 - col_11 (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 141.0,
'NumberOfInstances': 4998.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 141.0,
'NumberOfSymbolicFeatures': 0.0,
'... | ECG5000 | [
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f... | 1,202 |
363,772 | mean_absolute_error | mean_absolute_error | LimeSoda_O.32_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - wn_3800 (numeric)],
4: [4 - wn_3798.2 (numeric)],
5: [5 - wn_3796.3 (numeric)],
6: [6 - wn_3794.4 (numeric)],
7: [7 - wn_3792.6 (numeric)],
8: [8 - wn_3790.7 (numeric)],
9: [9 - wn_3788.8 (numeric)],
10: [10 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 1640.0,
'NumberOfInstances': 32.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1640.0,
'NumberOfSymbolicFeatures': 0.0,
'... | LimeSoda_O.32_dataset | [
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"Clay_target",
"wn_3800",
"wn_3798.2",
"wn_3796.3",
"wn_3794.4",
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"wn_3775.8",
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"wn_3770.2",
"wn_3768.3",
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f... | 1,203 |
13 | predictive_accuracy | accuracy_score | breast-cancer | **Author**:
**Source**: Unknown -
**Please cite**:
Citation Request:
This breast cancer domain was obtained from the University Medical Centre,
Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and
M. Soklic for providing the data. Please include this citation if you plan
... | {0: [0 - age (nominal)],
1: [1 - menopause (nominal)],
2: [2 - tumor-size (nominal)],
3: [3 - inv-nodes (nominal)],
4: [4 - node-caps (nominal)],
5: [5 - deg-malig (nominal)],
6: [6 - breast (nominal)],
7: [7 - breast-quad (nominal)],
8: [8 - irradiat (nominal)],
9: [9 - Class (nominal)]} | {'MajorityClassSize': 201.0,
'MaxNominalAttDistinctValues': 11.0,
'MinorityClassSize': 85.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 286.0,
'NumberOfInstancesWithMissingValues': 9.0,
'NumberOfMissingValues': 9.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 10.0,
... | breast-cancer | [
"age",
"menopause",
"tumor-size",
"inv-nodes",
"node-caps",
"deg-malig",
"breast",
"breast-quad",
"irradiat"
] | [
true,
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true,
true,
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true,
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] | 1,205 |
34 | predictive_accuracy | accuracy_score | postoperative-patient-data | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title: Postoperative Patient Data
2. Source Information:
-- Creators: Sharon Summers, School of Nursing, University of Kansas
Medical Center, Kansas City, KS 66160
Linda Woolery, School of Nursing, University of Mis... | {0: [0 - L-CORE (nominal)],
1: [1 - L-SURF (nominal)],
2: [2 - L-O2 (nominal)],
3: [3 - L-BP (nominal)],
4: [4 - SURF-STBL (nominal)],
5: [5 - CORE-STBL (nominal)],
6: [6 - BP-STBL (nominal)],
7: [7 - COMFORT (nominal)],
8: [8 - decision (nominal)]} | {'MajorityClassSize': 64.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 90.0,
'NumberOfInstancesWithMissingValues': 3.0,
'NumberOfMissingValues': 3.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 9.0,
'cost_... | postoperative-patient-data | [
"L-CORE",
"L-SURF",
"L-O2",
"L-BP",
"SURF-STBL",
"CORE-STBL",
"BP-STBL",
"COMFORT"
] | [
true,
true,
true,
true,
true,
true,
true,
true
] | 1,206 |
362,516 | mean_absolute_error | mean_absolute_error | Mercedes_Benz_Greener_Manufacturing | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "regression on categorical and
numerical features" benchmark. Original description:
Since the first a... | {0: [0 - X3 (nominal)],
1: [1 - X4 (nominal)],
2: [2 - X6 (nominal)],
3: [3 - X10 (nominal)],
4: [4 - X12 (nominal)],
5: [5 - X13 (nominal)],
6: [6 - X14 (nominal)],
7: [7 - X15 (nominal)],
8: [8 - X16 (nominal)],
9: [9 - X17 (nominal)],
10: [10 - X18 (nominal)],
11: [11 - X19 (nominal)],
12: [12 - X20 (nom... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 360.0,
'NumberOfInstances': 4209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 359.0,
'... | Mercedes_Benz_Greener_Manufacturing | [
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7 | predictive_accuracy | accuracy_score | audiology | **Author**: Professor Jergen at Baylor College of Medicine
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Audiology+(Standardized))
**Please cite**: Bareiss, E. Ray, & Porter, Bruce (1987). Protos: An Exemplar-Based Learning Apprentice. In the Proceedings of the 4th International Workshop on Machine Learning... | {0: [0 - age_gt_60 (nominal)],
1: [1 - air (nominal)],
2: [2 - airBoneGap (nominal)],
3: [3 - ar_c (nominal)],
4: [4 - ar_u (nominal)],
5: [5 - bone (nominal)],
6: [6 - boneAbnormal (nominal)],
7: [7 - bser (nominal)],
8: [8 - history_buzzing (nominal)],
9: [9 - history_dizziness (nominal)],
10: [10 - history... | {'MajorityClassSize': 57.0,
'MaxNominalAttDistinctValues': 24.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 24.0,
'NumberOfFeatures': 70.0,
'NumberOfInstances': 226.0,
'NumberOfInstancesWithMissingValues': 222.0,
'NumberOfMissingValues': 317.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 70.0... | audiology | [
"age_gt_60",
"air",
"airBoneGap",
"ar_c",
"ar_u",
"bone",
"boneAbnormal",
"bser",
"history_buzzing",
"history_dizziness",
"history_fluctuating",
"history_fullness",
"history_heredity",
"history_nausea",
"history_noise",
"history_recruitment",
"history_ringing",
"history_roaring",
... | [
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true... | 1,208 |
42 | predictive_accuracy | accuracy_score | haberman | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title: Haberman's Survival Data
2. Sources:
(a) Donor: Tjen-Sien Lim (limt@stat.wisc.edu)
(b) Date: March 4, 1999
3. Past Usage:
1. Haberman, S. J. (1976). Generalized Residuals for Log-Linear
Models, Proceedings of the 9th In... | {0: [0 - Age_of_patient_at_time_of_operation (numeric)],
1: [1 - Patients_year_of_operation (nominal)],
2: [2 - Number_of_positive_axillary_nodes_detected (numeric)],
3: [3 - Survival_status (nominal)]} | {'MajorityClassSize': 225.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 81.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 306.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | haberman | [
"Age_of_patient_at_time_of_operation",
"Patients_year_of_operation",
"Number_of_positive_axillary_nodes_detected"
] | [
false,
true,
false
] | 1,209 |
233,215 | root_mean_squared_error | root_mean_squared_error | Mercedes_Benz_Greener_Manufacturing | Since the first automobile, the Benz Patent Motor Car in 1886, Mercedes-Benz has stood for important automotive innovations. These include, for example, the passenger safety cell with crumple zone, the airbag and intelligent assistance systems. Mercedes-Benz applies for nearly 2000 patents per year, making the brand th... | {0: [0 - ID (numeric)],
1: [1 - y (numeric)],
2: [2 - X0 (nominal)],
3: [3 - X1 (nominal)],
4: [4 - X2 (nominal)],
5: [5 - X3 (nominal)],
6: [6 - X4 (nominal)],
7: [7 - X5 (nominal)],
8: [8 - X6 (nominal)],
9: [9 - X8 (nominal)],
10: [10 - X10 (numeric)],
11: [11 - X11 (numeric)],
12: [12 - X12 (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 377.0,
'NumberOfInstances': 4209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 369.0,
'NumberOfSymbolicFeatures': 8.0,
'... | Mercedes_Benz_Greener_Manufacturing | [
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... | 1,210 |
40 | predictive_accuracy | accuracy_score | glass | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title: Glass Identification Database
2. Sources:
(a) Creator: B. German
-- Central Research Establishment
Home Office Forensic Science Service
Aldermaston, Reading, Berkshire RG7 4PN
(b) Donor: Vina Spiehler, P... | {0: [0 - RI (numeric)],
1: [1 - Na (numeric)],
2: [2 - Mg (numeric)],
3: [3 - Al (numeric)],
4: [4 - Si (numeric)],
5: [5 - K (numeric)],
6: [6 - Ca (numeric)],
7: [7 - Ba (numeric)],
8: [8 - Fe (numeric)],
9: [9 - Type (nominal)]} | {'MajorityClassSize': 76.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 9.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 214.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | glass | [
"RI",
"Na",
"Mg",
"Al",
"Si",
"K",
"Ca",
"Ba",
"Fe"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,212 |
35 | predictive_accuracy | accuracy_score | dermatology | 1. Title: Dermatology Database
2. Source Information:
(a) Original owners:
-- 1. Nilsel Ilter, M.D., Ph.D.,
Gazi University,
School of Medicine
06510 Ankara, Turkey
Phone: +90 (312) 214 1080
-- 2. H. Altay Guvenir, PhD.,
Bilkent Univ... | {0: [0 - erythema (nominal)],
1: [1 - scaling (nominal)],
2: [2 - definite_borders (nominal)],
3: [3 - itching (nominal)],
4: [4 - koebner_phenomenon (nominal)],
5: [5 - polygonal_papules (nominal)],
6: [6 - follicular_papules (nominal)],
7: [7 - oral_mucosal_involvement (nominal)],
8: [8 - knee_and_elbow_invol... | {'MajorityClassSize': 112.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 35.0,
'NumberOfInstances': 366.0,
'NumberOfInstancesWithMissingValues': 8.0,
'NumberOfMissingValues': 8.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 34.0,
'... | dermatology | [
"erythema",
"scaling",
"definite_borders",
"itching",
"koebner_phenomenon",
"polygonal_papules",
"follicular_papules",
"oral_mucosal_involvement",
"knee_and_elbow_involvement",
"scalp_involvement",
"family_history",
"melanin_incontinence",
"eosinophils_in_the_infiltrate",
"PNL_infiltrate",... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false
] | 1,213 |
363,037 | mean_absolute_error | mean_absolute_error | ECG5000 | The original dataset for 'ECG5000' is a 20-hour long ECG downloaded from Physionet. The name is BIDMC Congestive Heart Failure Database(chfdb) and it is record 'chf07'. It was originally published in 'Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. Physi... | {0: [0 - col_0 (numeric)],
1: [1 - col_1 (numeric)],
2: [2 - col_2 (numeric)],
3: [3 - col_3 (numeric)],
4: [4 - col_4 (numeric)],
5: [5 - col_5 (numeric)],
6: [6 - col_6 (numeric)],
7: [7 - col_7 (numeric)],
8: [8 - col_8 (numeric)],
9: [9 - col_9 (numeric)],
10: [10 - col_10 (numeric)],
11: [11 - col_11 (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 141.0,
'NumberOfInstances': 4998.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 141.0,
'NumberOfSymbolicFeatures': 0.0,
'... | ECG5000 | [
"col_0",
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9",
"col_10",
"col_11",
"col_12",
"col_13",
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false,
false,
false,
false,
false,
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f... | 1,214 |
30 | predictive_accuracy | accuracy_score | page-blocks | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title of Database: Blocks Classification
2. Sources:
(a) Donato Malerba
Dipartimento di Informatica
University of Bari
via Orabona 4
70126 Bari - Italy
phone: +39 - 80 - 5443269
fax: +39 - 80 - 5443196
... | {0: [0 - height (numeric)],
1: [1 - lenght (numeric)],
2: [2 - area (numeric)],
3: [3 - eccen (numeric)],
4: [4 - p_black (numeric)],
5: [5 - p_and (numeric)],
6: [6 - mean_tr (numeric)],
7: [7 - blackpix (numeric)],
8: [8 - blackand (numeric)],
9: [9 - wb_trans (numeric)],
10: [10 - class (nominal)]} | {'MajorityClassSize': 4913.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 28.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 5473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | page-blocks | [
"height",
"lenght",
"area",
"eccen",
"p_black",
"p_and",
"mean_tr",
"blackpix",
"blackand",
"wb_trans"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,215 |
38 | predictive_accuracy | accuracy_score | ecoli | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title: Protein Localization Sites
2. Creator and Maintainer:
Kenta Nakai
Institue of Molecular and Cellular Biology
Osaka, University
1-3 Yamada-oka, Suita 565 Japan
nakai@imcb.osaka-u.ac.jp
http... | {0: [0 - mcg (numeric)],
1: [1 - gvh (numeric)],
2: [2 - lip (numeric)],
3: [3 - chg (numeric)],
4: [4 - aac (numeric)],
5: [5 - alm1 (numeric)],
6: [6 - alm2 (numeric)],
7: [7 - class (nominal)]} | {'MajorityClassSize': 143.0,
'MaxNominalAttDistinctValues': 8.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 336.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | ecoli | [
"mcg",
"gvh",
"lip",
"chg",
"aac",
"alm1",
"alm2"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,216 |
48 | predictive_accuracy | accuracy_score | heart-c | **Author**:
**Source**: Unknown -
**Please cite**:
Publication Request:
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
This file describes the contents of the heart-disease directory.
This directory contains 4 databases concerning heart disease diagnosis.
All attribu... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - cp (nominal)],
3: [3 - trestbps (numeric)],
4: [4 - chol (numeric)],
5: [5 - fbs (nominal)],
6: [6 - restecg (nominal)],
7: [7 - thalach (numeric)],
8: [8 - exang (nominal)],
9: [9 - oldpeak (numeric)],
10: [10 - slope (nominal)],
11: [11 - ca (numeric... | {'MajorityClassSize': 165.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 138.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 303.0,
'NumberOfInstancesWithMissingValues': 7.0,
'NumberOfMissingValues': 7.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 8.0,
'... | heart-c | [
"age",
"sex",
"cp",
"trestbps",
"chol",
"fbs",
"restecg",
"thalach",
"exang",
"oldpeak",
"slope",
"ca",
"thal"
] | [
false,
true,
true,
false,
false,
true,
true,
false,
true,
false,
true,
false,
true
] | 1,217 |
52 | predictive_accuracy | accuracy_score | heart-statlog | **Author**:
**Source**: Unknown -
**Please cite**:
This database contains 13 attributes (which have been extracted from
a larger set of 75)
Attribute Information:
------------------------
-- 1. age
-- 2. sex
-- 3. chest pain type (4 values)
-... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - chest (numeric)],
3: [3 - resting_blood_pressure (numeric)],
4: [4 - serum_cholestoral (numeric)],
5: [5 - fasting_blood_sugar (numeric)],
6: [6 - resting_electrocardiographic_results (numeric)],
7: [7 - maximum_heart_rate_achieved (numeric)],
8: [8 - exe... | {'MajorityClassSize': 150.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 120.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 270.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 1.0,
... | heart-statlog | [
"age",
"sex",
"chest",
"resting_blood_pressure",
"serum_cholestoral",
"fasting_blood_sugar",
"resting_electrocardiographic_results",
"maximum_heart_rate_achieved",
"exercise_induced_angina",
"oldpeak",
"slope",
"number_of_major_vessels",
"thal"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,218 |
363,769 | mean_absolute_error | mean_absolute_error | LimeSoda_NRW.42_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - wn_3799 (numeric)],
4: [4 - wn_3797.1 (numeric)],
5: [5 - wn_3795.1 (numeric)],
6: [6 - wn_3793.2 (numeric)],
7: [7 - wn_3791.3 (numeric)],
8: [8 - wn_3789.3 (numeric)],
9: [9 - wn_3787.4 (numeric)],
10: [10 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 1689.0,
'NumberOfInstances': 42.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1689.0,
'NumberOfSymbolicFeatures': 0.0,
'... | LimeSoda_NRW.42_dataset | [
"pH_target",
"Clay_target",
"wn_3799",
"wn_3797.1",
"wn_3795.1",
"wn_3793.2",
"wn_3791.3",
"wn_3789.3",
"wn_3787.4",
"wn_3785.5",
"wn_3783.6",
"wn_3781.6",
"wn_3779.7",
"wn_3777.8",
"wn_3775.8",
"wn_3773.9",
"wn_3772",
"wn_3770.1",
"wn_3768.1",
"wn_3766.2",
"wn_3764.3",
"wn... | [
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f... | 1,220 |
50 | predictive_accuracy | accuracy_score | heart-h | **Author**:
**Source**: Unknown -
**Please cite**:
Publication Request:
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
This file describes the contents of the heart-disease directory.
This directory contains 4 databases concerning heart disease diagnosis.
All attribu... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - chest_pain (nominal)],
3: [3 - trestbps (numeric)],
4: [4 - chol (numeric)],
5: [5 - fbs (nominal)],
6: [6 - restecg (nominal)],
7: [7 - thalach (numeric)],
8: [8 - exang (nominal)],
9: [9 - oldpeak (numeric)],
10: [10 - slope (nominal)],
11: [11 - ca ... | {'MajorityClassSize': 188.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 106.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 294.0,
'NumberOfInstancesWithMissingValues': 293.0,
'NumberOfMissingValues': 782.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 8.0... | heart-h | [
"age",
"sex",
"chest_pain",
"trestbps",
"chol",
"fbs",
"restecg",
"thalach",
"exang",
"oldpeak",
"slope",
"ca",
"thal"
] | [
false,
true,
true,
false,
false,
true,
true,
false,
true,
false,
true,
false,
true
] | 1,221 |
39 | predictive_accuracy | accuracy_score | sonar | **Author**:
**Source**: Unknown -
**Please cite**:
NAME: Sonar, Mines vs. Rocks
SUMMARY: This is the data set used by Gorman and Sejnowski in their study
of the classification of sonar signals using a neural network [1]. The
task is to train a network to discriminate between sonar signals bounced
off a... | {0: [0 - attribute_1 (numeric)],
1: [1 - attribute_2 (numeric)],
2: [2 - attribute_3 (numeric)],
3: [3 - attribute_4 (numeric)],
4: [4 - attribute_5 (numeric)],
5: [5 - attribute_6 (numeric)],
6: [6 - attribute_7 (numeric)],
7: [7 - attribute_8 (numeric)],
8: [8 - attribute_9 (numeric)],
9: [9 - attribute_10 (... | {'MajorityClassSize': 111.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 97.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 208.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 60.0,
'NumberOfSymbolicFeatures': 1.0,
'... | sonar | [
"attribute_1",
"attribute_2",
"attribute_3",
"attribute_4",
"attribute_5",
"attribute_6",
"attribute_7",
"attribute_8",
"attribute_9",
"attribute_10",
"attribute_11",
"attribute_12",
"attribute_13",
"attribute_14",
"attribute_15",
"attribute_16",
"attribute_17",
"attribute_18",
"... | [
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false,
false,
false,
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false,
false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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f... | 1,222 |
54 | predictive_accuracy | accuracy_score | hepatitis | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title: Hepatitis Domain
2. Sources:
(a) unknown
(b) Donor: G.Gong (Carnegie-Mellon University) via
Bojan Cestnik
Jozef Stefan Institute
Jamova 39
61000 Ljublja... | {0: [0 - AGE (numeric)],
1: [1 - SEX (nominal)],
2: [2 - STEROID (nominal)],
3: [3 - ANTIVIRALS (nominal)],
4: [4 - FATIGUE (nominal)],
5: [5 - MALAISE (nominal)],
6: [6 - ANOREXIA (nominal)],
7: [7 - LIVER_BIG (nominal)],
8: [8 - LIVER_FIRM (nominal)],
9: [9 - SPLEEN_PALPABLE (nominal)],
10: [10 - SPIDERS (n... | {'MajorityClassSize': 123.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 32.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 155.0,
'NumberOfInstancesWithMissingValues': 75.0,
'NumberOfMissingValues': 167.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 14.0,... | hepatitis | [
"AGE",
"SEX",
"STEROID",
"ANTIVIRALS",
"FATIGUE",
"MALAISE",
"ANOREXIA",
"LIVER_BIG",
"LIVER_FIRM",
"SPLEEN_PALPABLE",
"SPIDERS",
"ASCITES",
"VARICES",
"BILIRUBIN",
"ALK_PHOSPHATE",
"SGOT",
"ALBUMIN",
"PROTIME",
"HISTOLOGY"
] | [
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
true
] | 1,223 |
47 | predictive_accuracy | accuracy_score | tae | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title: Teaching Assistant Evaluation
2. Sources:
(a) Collector: Wei-Yin Loh (Department of Statistics, UW-Madison)
(b) Donor: Tjen-Sien Lim (limt@stat.wisc.edu)
(b) Date: June 7, 1997
3. Past Usage:
1. Loh, W.-Y. & Shih, Y.-S... | {0: [0 - Whether_of_not_the_TA_is_a_native_English_speaker (nominal)],
1: [1 - Course_instructor (numeric)],
2: [2 - Course (numeric)],
3: [3 - Summer_or_regular_semester (nominal)],
4: [4 - Class_size (numeric)],
5: [5 - Class_attribute (nominal)]} | {'MajorityClassSize': 52.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 49.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 151.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 3.0,
'cos... | tae | [
"Whether_of_not_the_TA_is_a_native_English_speaker",
"Course_instructor",
"Course",
"Summer_or_regular_semester",
"Class_size"
] | [
true,
false,
false,
true,
false
] | 1,224 |
5 | predictive_accuracy | accuracy_score | arrhythmia | **Author**: H. Altay Guvenir, Burak Acar, Haldun Muderrisoglu
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/arrhythmia)
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**Cardiac Arrhythmia Database**
The aim is to determine the type of arrhythmia from the ECG recordings. ... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - height (numeric)],
3: [3 - weight (numeric)],
4: [4 - QRSduration (numeric)],
5: [5 - PRinterval (numeric)],
6: [6 - Q-Tinterval (numeric)],
7: [7 - Tinterval (numeric)],
8: [8 - Pinterval (numeric)],
9: [9 - QRS (numeric)],
10: [10 - T (numeric)],
11:... | {'MajorityClassSize': 245.0,
'MaxNominalAttDistinctValues': 13.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 13.0,
'NumberOfFeatures': 280.0,
'NumberOfInstances': 452.0,
'NumberOfInstancesWithMissingValues': 384.0,
'NumberOfMissingValues': 408.0,
'NumberOfNumericFeatures': 206.0,
'NumberOfSymbolicFeatures': ... | arrhythmia | [
"age",
"sex",
"height",
"weight",
"QRSduration",
"PRinterval",
"Q-Tinterval",
"Tinterval",
"Pinterval",
"QRS",
"T",
"P",
"QRST",
"J",
"heartrate",
"chDI_Qwave",
"chDI_Rwave",
"chDI_Swave",
"chDI_RPwave",
"chDI_SPwave",
"chDI_intrinsicReflecttions",
"chDI_RRwaveExists",
"c... | [
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true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
true,
tr... | 1,225 |
363,770 | mean_absolute_error | mean_absolute_error | LimeSoda_NRW.62_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - wn_3799 (numeric)],
4: [4 - wn_3797.1 (numeric)],
5: [5 - wn_3795.1 (numeric)],
6: [6 - wn_3793.2 (numeric)],
7: [7 - wn_3791.3 (numeric)],
8: [8 - wn_3789.3 (numeric)],
9: [9 - wn_3787.4 (numeric)],
10: [10 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 1689.0,
'NumberOfInstances': 62.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1689.0,
'NumberOfSymbolicFeatures': 0.0,
'... | LimeSoda_NRW.62_dataset | [
"pH_target",
"Clay_target",
"wn_3799",
"wn_3797.1",
"wn_3795.1",
"wn_3793.2",
"wn_3791.3",
"wn_3789.3",
"wn_3787.4",
"wn_3785.5",
"wn_3783.6",
"wn_3781.6",
"wn_3779.7",
"wn_3777.8",
"wn_3775.8",
"wn_3773.9",
"wn_3772",
"wn_3770.1",
"wn_3768.1",
"wn_3766.2",
"wn_3764.3",
"wn... | [
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f... | 1,226 |
363,774 | mean_absolute_error | mean_absolute_error | LimeSoda_RP.62_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - ERa (numeric)],
4: [4 - G_Total_Counts (numeric)],
5: [5 - G_K (numeric)],
6: [6 - G_U (numeric)],
7: [7 - G_Th (numeric)],
8: [8 - G_Cs (numeric)],
9: [9 - wl_1000 (numeric)],
10: [10 - wl_1001 (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
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'NumberOfClasses': 0.0,
'NumberOfFeatures': 1413.0,
'NumberOfInstances': 62.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1413.0,
'NumberOfSymbolicFeatures': 0.0,
'... | LimeSoda_RP.62_dataset | [
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"Clay_target",
"ERa",
"G_Total_Counts",
"G_K",
"G_U",
"G_Th",
"G_Cs",
"wl_1000",
"wl_1001",
"wl_1002",
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"wl_1004",
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"wl_1006",
"wl_1007",
"wl_1008",
"wl_1009",
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"wl_1012",
"wl_1013",
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"wl_1016"... | [
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f... | 1,227 |
359,942 | root_mean_squared_error | root_mean_squared_error | colleges | Modified version for the automl benchmark.
Regroups information for about 7800 different US colleges. Including geographical information, stats about the population attending and post graduation career earnings. | {0: [0 - UNITID (numeric)],
1: [1 - school_name (string)],
2: [2 - city (nominal)],
3: [3 - state (nominal)],
4: [4 - zip (nominal)],
5: [5 - school_webpage (string)],
6: [6 - latitude (numeric)],
7: [7 - longitude (numeric)],
8: [8 - admission_rate (numeric)],
9: [9 - sat_verbal_midrange (numeric)],
10: [10 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 45.0,
'NumberOfInstances': 7063.0,
'NumberOfInstancesWithMissingValues': 7063.0,
'NumberOfMissingValues': 104249.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 1... | colleges | [
"city",
"state",
"zip",
"latitude",
"longitude",
"admission_rate",
"sat_verbal_midrange",
"sat_math_midrange",
"sat_writing_midrange",
"act_combined_midrange",
"act_english_midrange",
"act_math_midrange",
"act_writing_midrange",
"sat_total_average",
"undergrad_size",
"percent_white",
... | [
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60 | predictive_accuracy | accuracy_score | zoo | **Author**: Richard S. Forsyth
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Zoo) - 5/15/1990
**Please cite**:
**Zoo database**
A simple database containing 17 Boolean-valued attributes describing animals. The "type" attribute appears to be the class attribute.
Notes:
* I find it unusual tha... | {0: [0 - animal (nominal)],
1: [1 - hair (nominal)],
2: [2 - feathers (nominal)],
3: [3 - eggs (nominal)],
4: [4 - milk (nominal)],
5: [5 - airborne (nominal)],
6: [6 - aquatic (nominal)],
7: [7 - predator (nominal)],
8: [8 - toothed (nominal)],
9: [9 - backbone (nominal)],
10: [10 - breathes (nominal)],
11:... | {'MajorityClassSize': 41.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 4.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 101.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 16.0,
'co... | zoo | [
"hair",
"feathers",
"eggs",
"milk",
"airborne",
"aquatic",
"predator",
"toothed",
"backbone",
"breathes",
"venomous",
"fins",
"legs",
"tail",
"domestic",
"catsize"
] | [
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] | 1,229 |
2 | predictive_accuracy | accuracy_score | anneal | **Author**: Unknown. Donated by David Sterling and Wray Buntine
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Annealing) - 1990
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
The original Annealing dataset from UCI. The exact meaning of the features and classes is lar... | {0: [0 - family (nominal)],
1: [1 - product-type (nominal)],
2: [2 - steel (nominal)],
3: [3 - carbon (numeric)],
4: [4 - hardness (numeric)],
5: [5 - temper_rolling (nominal)],
6: [6 - condition (nominal)],
7: [7 - formability (nominal)],
8: [8 - strength (numeric)],
9: [9 - non-ageing (nominal)],
10: [10 - ... | {'MajorityClassSize': 684.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 8.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 898.0,
'NumberOfMissingValues': 22175.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.... | anneal | [
"family",
"product-type",
"steel",
"carbon",
"hardness",
"temper_rolling",
"condition",
"formability",
"strength",
"non-ageing",
"surface-finish",
"surface-quality",
"enamelability",
"bc",
"bf",
"bt",
"bw%2Fme",
"bl",
"m",
"chrom",
"phos",
"cbond",
"marvi",
"exptl",
"... | [
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] | 1,230 |
363,777 | mean_absolute_error | mean_absolute_error | LimeSoda_SC.93_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - wl_355 (numeric)],
4: [4 - wl_356 (numeric)],
5: [5 - wl_357 (numeric)],
6: [6 - wl_358 (numeric)],
7: [7 - wl_359 (numeric)],
8: [8 - wl_360 (numeric)],
9: [9 - wl_361 (numeric)],
10: [10 - wl_362 (numeric)]... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 2149.0,
'NumberOfInstances': 93.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2149.0,
'NumberOfSymbolicFeatures': 0.0,
'... | LimeSoda_SC.93_dataset | [
"pH_target",
"Clay_target",
"wl_355",
"wl_356",
"wl_357",
"wl_358",
"wl_359",
"wl_360",
"wl_361",
"wl_362",
"wl_363",
"wl_364",
"wl_365",
"wl_366",
"wl_367",
"wl_368",
"wl_369",
"wl_370",
"wl_371",
"wl_372",
"wl_373",
"wl_374",
"wl_375",
"wl_376",
"wl_377",
"wl_378"... | [
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f... | 1,231 |
57 | predictive_accuracy | accuracy_score | ionosphere | **Author**: Space Physics Group, Applied Physics Laboratory, Johns Hopkins University. Donated by Vince Sigillito.
**Source**: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/ionosphere)
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**Johns Hopkins Universit... | {0: [0 - a01 (numeric)],
1: [1 - a02 (numeric)],
2: [2 - a03 (numeric)],
3: [3 - a04 (numeric)],
4: [4 - a05 (numeric)],
5: [5 - a06 (numeric)],
6: [6 - a07 (numeric)],
7: [7 - a08 (numeric)],
8: [8 - a09 (numeric)],
9: [9 - a10 (numeric)],
10: [10 - a11 (numeric)],
11: [11 - a12 (numeric)],
12: [12 - a13 (... | {'MajorityClassSize': 225.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 126.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 35.0,
'NumberOfInstances': 351.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 34.0,
'NumberOfSymbolicFeatures': 1.0,
... | ionosphere | [
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"a02",
"a03",
"a04",
"a05",
"a06",
"a07",
"a08",
"a09",
"a10",
"a11",
"a12",
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"a16",
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"a20",
"a21",
"a22",
"a23",
"a24",
"a25",
"a26",
"a27",
"a28",
"a29",
"a30",
"a31",
"a32",
"a33",
"a34"
] | [
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] | 1,232 |
59 | predictive_accuracy | accuracy_score | iris | **Author**: R.A. Fisher
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Iris) - 1936 - Donated by Michael Marshall
**Please cite**:
**Iris Plants Database**
This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is ref... | {0: [0 - sepallength (numeric)],
1: [1 - sepalwidth (numeric)],
2: [2 - petallength (numeric)],
3: [3 - petalwidth (numeric)],
4: [4 - class (nominal)]} | {'MajorityClassSize': 50.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 50.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 150.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | iris | [
"sepallength",
"sepalwidth",
"petallength",
"petalwidth"
] | [
false,
false,
false,
false
] | 1,233 |
363,768 | mean_absolute_error | mean_absolute_error | LimeSoda_NRW.115_dataset | Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1... | {0: [0 - SOC_target (numeric)],
1: [1 - pH_target (numeric)],
2: [2 - Clay_target (numeric)],
3: [3 - wn_3799 (numeric)],
4: [4 - wn_3797.1 (numeric)],
5: [5 - wn_3795.1 (numeric)],
6: [6 - wn_3793.2 (numeric)],
7: [7 - wn_3791.3 (numeric)],
8: [8 - wn_3789.3 (numeric)],
9: [9 - wn_3787.4 (numeric)],
10: [10 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 1689.0,
'NumberOfInstances': 115.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1689.0,
'NumberOfSymbolicFeatures': 0.0,
... | LimeSoda_NRW.115_dataset | [
"pH_target",
"Clay_target",
"wn_3799",
"wn_3797.1",
"wn_3795.1",
"wn_3793.2",
"wn_3791.3",
"wn_3789.3",
"wn_3787.4",
"wn_3785.5",
"wn_3783.6",
"wn_3781.6",
"wn_3779.7",
"wn_3777.8",
"wn_3775.8",
"wn_3773.9",
"wn_3772",
"wn_3770.1",
"wn_3768.1",
"wn_3766.2",
"wn_3764.3",
"wn... | [
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f... | 1,234 |
239 | predictive_accuracy | accuracy_score | autos | **Author**: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Automobile) - 1987
**Please cite**:
**1985 Auto Imports Database**
This data set consists of three types of entities: (a) the specification of an auto in terms of various characteris... | {0: [0 - normalized-losses (numeric)],
1: [1 - make (nominal)],
2: [2 - fuel-type (nominal)],
3: [3 - aspiration (nominal)],
4: [4 - num-of-doors (nominal)],
5: [5 - body-style (nominal)],
6: [6 - drive-wheels (nominal)],
7: [7 - engine-location (nominal)],
8: [8 - wheel-base (numeric)],
9: [9 - length (numeri... | {'MajorityClassSize': 67.0,
'MaxNominalAttDistinctValues': 22.0,
'MinorityClassSize': 3.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 205.0,
'NumberOfInstancesWithMissingValues': 46.0,
'NumberOfMissingValues': 59.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 11.0,
... | autos | [
"normalized-losses",
"make",
"fuel-type",
"aspiration",
"num-of-doors",
"body-style",
"drive-wheels",
"engine-location",
"wheel-base",
"length",
"width",
"height",
"curb-weight",
"engine-type",
"num-of-cylinders",
"engine-size",
"fuel-system",
"bore",
"stroke",
"compression-rat... | [
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false,
true,
true,
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true,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,235 |
55 | predictive_accuracy | accuracy_score | vote | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title: 1984 United States Congressional Voting Records Database
2. Source Information:
(a) Source: Congressional Quarterly Almanac, 98th Congress,
2nd session 1984, Volume XL: Congressional Quarterly Inc.
Wash... | {0: [0 - handicapped-infants (nominal)],
1: [1 - water-project-cost-sharing (nominal)],
2: [2 - adoption-of-the-budget-resolution (nominal)],
3: [3 - physician-fee-freeze (nominal)],
4: [4 - el-salvador-aid (nominal)],
5: [5 - religious-groups-in-schools (nominal)],
6: [6 - anti-satellite-test-ban (nominal)],
7:... | {'MajorityClassSize': 267.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 168.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 435.0,
'NumberOfInstancesWithMissingValues': 203.0,
'NumberOfMissingValues': 392.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 17.... | vote | [
"handicapped-infants",
"water-project-cost-sharing",
"adoption-of-the-budget-resolution",
"physician-fee-freeze",
"el-salvador-aid",
"religious-groups-in-schools",
"anti-satellite-test-ban",
"aid-to-nicaraguan-contras",
"mx-missile",
"immigration",
"synfuels-corporation-cutback",
"education-sp... | [
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,236 |
234 | predictive_accuracy | accuracy_score | labor | **Author**: Unknown
**Source**: Collective Barganing Review, Labour Canada
**Please cite**: https://archive.ics.uci.edu/ml/citation_policy.html
Date: Tue, 15 Nov 88 15:44:08 EST
From: stan <stan@csi2.UofO.EDU>
To: aha@ICS.UCI.EDU
1. Title: Final settlements in labor negotitions in Canadian industry
2. Source I... | {0: [0 - duration (numeric)],
1: [1 - wage-increase-first-year (numeric)],
2: [2 - wage-increase-second-year (numeric)],
3: [3 - wage-increase-third-year (numeric)],
4: [4 - cost-of-living-adjustment (nominal)],
5: [5 - working-hours (numeric)],
6: [6 - pension (nominal)],
7: [7 - standby-pay (numeric)],
8: [8 ... | {'MajorityClassSize': 37.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 57.0,
'NumberOfInstancesWithMissingValues': 56.0,
'NumberOfMissingValues': 326.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 9.0,
'... | labor | [
"duration",
"wage-increase-first-year",
"wage-increase-second-year",
"wage-increase-third-year",
"cost-of-living-adjustment",
"working-hours",
"pension",
"standby-pay",
"shift-differential",
"education-allowance",
"statutory-holidays",
"vacation",
"longterm-disability-assistance",
"contrib... | [
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true,
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true,
true,
true,
true,
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] | 1,237 |
240 | predictive_accuracy | accuracy_score | lymph | **Author**:
**Source**: Unknown -
**Please cite**:
Citation Request:
This lymphography domain was obtained from the University Medical Centre,
Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and
M. Soklic for providing the data. Please include this citation if you plan
... | {0: [0 - lymphatics (nominal)],
1: [1 - block_of_affere (nominal)],
2: [2 - bl_of_lymph_c (nominal)],
3: [3 - bl_of_lymph_s (nominal)],
4: [4 - by_pass (nominal)],
5: [5 - extravasates (nominal)],
6: [6 - regeneration_of (nominal)],
7: [7 - early_uptake_in (nominal)],
8: [8 - lym_nodes_dimin (numeric)],
9: [9 ... | {'MajorityClassSize': 81.0,
'MaxNominalAttDistinctValues': 8.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 148.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 16.0,
'co... | lymph | [
"lymphatics",
"block_of_affere",
"bl_of_lymph_c",
"bl_of_lymph_s",
"by_pass",
"extravasates",
"regeneration_of",
"early_uptake_in",
"lym_nodes_dimin",
"lym_nodes_enlar",
"changes_in_lym",
"defect_in_node",
"changes_in_node",
"changes_in_stru",
"special_forms",
"dislocation_of",
"excl... | [
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] | 1,238 |
237 | predictive_accuracy | accuracy_score | audiology | **Author**: Professor Jergen at Baylor College of Medicine
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Audiology+(Standardized))
**Please cite**: Bareiss, E. Ray, & Porter, Bruce (1987). Protos: An Exemplar-Based Learning Apprentice. In the Proceedings of the 4th International Workshop on Machine Learning... | {0: [0 - age_gt_60 (nominal)],
1: [1 - air (nominal)],
2: [2 - airBoneGap (nominal)],
3: [3 - ar_c (nominal)],
4: [4 - ar_u (nominal)],
5: [5 - bone (nominal)],
6: [6 - boneAbnormal (nominal)],
7: [7 - bser (nominal)],
8: [8 - history_buzzing (nominal)],
9: [9 - history_dizziness (nominal)],
10: [10 - history... | {'MajorityClassSize': 57.0,
'MaxNominalAttDistinctValues': 24.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 24.0,
'NumberOfFeatures': 70.0,
'NumberOfInstances': 226.0,
'NumberOfInstancesWithMissingValues': 222.0,
'NumberOfMissingValues': 317.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 70.0... | audiology | [
"age_gt_60",
"air",
"airBoneGap",
"ar_c",
"ar_u",
"bone",
"boneAbnormal",
"bser",
"history_buzzing",
"history_dizziness",
"history_fluctuating",
"history_fullness",
"history_heredity",
"history_nausea",
"history_noise",
"history_recruitment",
"history_ringing",
"history_roaring",
... | [
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,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 1,239 |
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