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361,127 | predictive_accuracy | accuracy_score | KDDCup09_upselling | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "classification on categorical and
numerical features" benchmark. Original description:
**Author**:
**Source**: Un... | {0: [0 - Var6 (numeric)],
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75,090 | predictive_accuracy | accuracy_score | gina_prior2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
**Note: Identical to the MNIST dataset?**
Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch)
Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php
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146,594 | predictive_accuracy | accuracy_score | madelon | **Author**: Isabelle Guyon
**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/madelon)
**Please cite**: Isabelle Guyon, Steve R. Gunn, Asa Ben-Hur, Gideon Dror, 2004. Result analysis of the NIPS 2003 feature selection challenge.
#### Abstract:
MADELON is an artificial dataset, which was part of the NIPS 20... | {0: [0 - V1 (numeric)],
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362,297 | predictive_accuracy | accuracy_score | Midwest_survey | Survey to know if people self-identify as Midwesterners. | {0: [0 - RespondentID (numeric)],
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362,345 | predictive_accuracy | accuracy_score | 1StudentPerfromance | #StudentPerfromance | {0: [0 - gender (nominal)],
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2: [2 - parental level of education (nominal)],
3: [3 - lunch (nominal)],
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5: [5 - math score (numeric)],
6: [6 - reading score (numeric)],
7: [7 - writing score (numeric)]} | {'MajorityClassSize': 518.0,
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'... | 1StudentPerfromance | [
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361,318 | predictive_accuracy | accuracy_score | qsar | The QSAR biodegradation dataset was built in the Milano Chemometrics and QSAR Research Group. The research leading to these results has received funding from the European Communitys Seventh Framework Programme [FP7/2007-2013] under Grant Agreement n. 238701 of Marie Curie ITN Environmental Chemoinformatics (ECO) projec... | {0: [0 - 0 (numeric)],
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361,200 | predictive_accuracy | accuracy_score | Meta_Album_DOG_Mini | ## **Meta-Album Dogs Dataset (Mini)**
***
Researchers from Stanford University created the original Dogs dataset (http://vision.stanford.edu/aditya86/ImageNetDogs/). It contains more than 20 000 images belonging to 120 breeds of dogs worldwide. The images and annotations came from ImageNet for the task of fine-grained ... | {0: [0 - FILE_NAME (string)],
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'NumberOfSymbolicFeatures': 0... | Meta_Album_DOG_Mini | [
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362,396 | predictive_accuracy | accuracy_score | iriiiiiis | will delete again | {0: [0 - Sepal.Length (numeric)],
1: [1 - Sepal.Width (numeric)],
2: [2 - Petal.Length (numeric)],
3: [3 - Petal.Width (numeric)],
4: [4 - Species (nominal)],
5: [5 - id (numeric)]} | {'MajorityClassSize': 50.0,
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362,303 | predictive_accuracy | accuracy_score | Midwest_survey | Survey to know if people self-identify as Midwesterners. | {0: [0 - RespondentID (numeric)],
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4: [4 - Do_you_consider_Indiana_state_as_part_of_the_Mi... | {'MajorityClassSize': 758.0,
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'NumberOfInstancesWithMissingValues': 74.0,
'NumberOfMissingValues': 99.0,
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'NumberOfSymbolicFeatures': 25.0,... | Midwest_survey | [
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361,316 | predictive_accuracy | accuracy_score | KDD | This dataset is for classification tasks, and has both continuous and categorical variables. | {0: [0 - Var6 (numeric)],
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362,302 | predictive_accuracy | accuracy_score | Midwest_survey | Survey to know if people self-identify as Midwesterners. | {0: [0 - RespondentID (numeric)],
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362,315 | predictive_accuracy | accuracy_score | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | arbres-urbains | {0: [0 - id_arbre (nominal)],
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10: [1... | {'MajorityClassSize': 398.0,
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'NumberOfMissingValues': 7889.0,
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362,416 | predictive_accuracy | accuracy_score | eucalyptus | jobScheduling: HPC Job Scheduling Data
In AppliedPredictiveModeling: Functions and Data Sets for 'Applied Predictive Modeling' | {0: [0 - Protocol (nominal)],
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... | eucalyptus | [
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362,415 | predictive_accuracy | accuracy_score | eucalyptus | Find out which seedlots in a species are best for soil conservation in dry hill country. Bulluch B. T., (1992) Eucalyptus Species Selection for Soil Conservation in Seasonally Dry Hill Country - Twelfth Year Assessment New Zealand Journal of Forestry Science 21(1): 10 - 31 (1991)
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7: [7 - Frosts (numeric)],
8: [8 - Year (numeric)],
9: [9 - Sp (nominal)],
10: [10 - PMCno (nominal)],
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362,420 | predictive_accuracy | accuracy_score | anneal | null | {0: [0 - family (nominal)],
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10: [10 - ... | {'MajorityClassSize': 486.0,
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'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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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true,
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false,
false,
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] | 2,949 |
361,299 | predictive_accuracy | accuracy_score | jasmine | This is a "supervised learning" challenge in machine learning. We are making available 30 datasets, all pre-formatted in given feature representations (this means that each example consists of a fixed number of numerical coefficients). The challenge is to solve classification and regression problems, without any furthe... | {0: [0 - V13 (numeric)],
1: [1 - V23 (numeric)],
2: [2 - V43 (numeric)],
3: [3 - V45 (numeric)],
4: [4 - V56 (numeric)],
5: [5 - V59 (numeric)],
6: [6 - V126 (numeric)],
7: [7 - V131 (numeric)],
8: [8 - V1 (nominal)],
9: [9 - V2 (nominal)],
10: [10 - V3 (nominal)],
11: [11 - V4 (nominal)],
12: [12 - V5 (nom... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 145.0,
'NumberOfInstances': 2984.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 136.0,
'... | jasmine | [
"V13",
"V23",
"V43",
"V45",
"V56",
"V59",
"V126",
"V131",
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
... | [
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 2,950 |
362,335 | predictive_accuracy | accuracy_score | Weather-11.10 | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather-11.10 | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 2,951 |
361,214 | predictive_accuracy | accuracy_score | Meta_Album_FLW_Extended | ## **Meta-Album Flowers Dataset (Extended)**
***
The Flowers dataset(https://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) consists of a variety of flowers gathered from different websites and some are photographed by the original creators. These flowers are commonly found in the UK. The images generally have l... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 258.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 102.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 8189.0,
'NumberOfInstancesWithMissingValues': 8189.0,
'NumberOfMissingValues': 8189.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': ... | Meta_Album_FLW_Extended | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,952 |
362,314 | predictive_accuracy | accuracy_score | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | Arbres urbains | {0: [0 - id_arbre (nominal)],
1: [1 - commune (nominal)],
2: [2 - quartier (nominal)],
3: [3 - site (nominal)],
4: [4 - cote_voirie (nominal)],
5: [5 - matricule_arbre (numeric)],
6: [6 - genre_arbre (nominal)],
7: [7 - espece_arbre (nominal)],
8: [8 - controle (numeric)],
9: [9 - situation (nominal)],
10: [1... | {'MajorityClassSize': 398.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 10.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 57.0,
'NumberOfInstances': 699.0,
'NumberOfInstancesWithMissingValues': 699.0,
'NumberOfMissingValues': 7889.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 45... | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | [
"id_arbre",
"commune",
"quartier",
"site",
"cote_voirie",
"matricule_arbre",
"genre_arbre",
"espece_arbre",
"controle",
"situation",
"type_sol",
"surf_permeable",
"date_plantation",
"classe_age",
"hauteur",
"classe_hauteur",
"diametre",
"circonference-en-cm",
"classe_circonferenc... | [
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,... | 2,953 |
362,422 | predictive_accuracy | accuracy_score | anneal | null | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
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true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,954 |
362,316 | predictive_accuracy | accuracy_score | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | arbres-urbains | {0: [0 - id_arbre (nominal)],
1: [1 - commune (nominal)],
2: [2 - quartier (nominal)],
3: [3 - site (nominal)],
4: [4 - cote_voirie (nominal)],
5: [5 - matricule_arbre (numeric)],
6: [6 - genre_arbre (nominal)],
7: [7 - espece_arbre (nominal)],
8: [8 - controle (numeric)],
9: [9 - situation (nominal)],
10: [1... | {'MajorityClassSize': 398.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 10.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 57.0,
'NumberOfInstances': 699.0,
'NumberOfInstancesWithMissingValues': 699.0,
'NumberOfMissingValues': 7889.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 45... | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | [
"id_arbre",
"commune",
"quartier",
"site",
"cote_voirie",
"matricule_arbre",
"genre_arbre",
"espece_arbre",
"controle",
"situation",
"type_sol",
"surf_permeable",
"date_plantation",
"classe_age",
"hauteur",
"classe_hauteur",
"diametre",
"circonference-en-cm",
"classe_circonferenc... | [
true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,... | 2,955 |
362,413 | predictive_accuracy | accuracy_score | mushroom | Mushroom records drawn from The Audubon Society Field Guide to North American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred A. Knopf | {0: [0 - class (nominal)],
1: [1 - cap_shape (nominal)],
2: [2 - cap_surface (nominal)],
3: [3 - cap_color (nominal)],
4: [4 - bruises (nominal)],
5: [5 - odor (nominal)],
6: [6 - gill_attachment (nominal)],
7: [7 - gill_spacing (nominal)],
8: [8 - gill_size (nominal)],
9: [9 - gill_color (nominal)],
10: [10 ... | {'MajorityClassSize': 4208.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3916.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 8124.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 23.0... | mushroom | [
"cap_shape",
"cap_surface",
"cap_color",
"bruises",
"odor",
"gill_attachment",
"gill_spacing",
"gill_size",
"gill_color",
"stalk_shape",
"stalk_root",
"stalk_surface_above_ring",
"stalk_surface_below_ring",
"stalk_color_above_ring",
"stalk_color_below_ring",
"veil_type",
"veil_color"... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,956 |
362,313 | predictive_accuracy | accuracy_score | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | Arbres urbains | {0: [0 - id_arbre (nominal)],
1: [1 - commune (nominal)],
2: [2 - quartier (nominal)],
3: [3 - site (nominal)],
4: [4 - cote_voirie (nominal)],
5: [5 - matricule_arbre (numeric)],
6: [6 - genre_arbre (nominal)],
7: [7 - espece_arbre (nominal)],
8: [8 - controle (numeric)],
9: [9 - situation (nominal)],
10: [1... | {'MajorityClassSize': 398.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 10.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 57.0,
'NumberOfInstances': 709.0,
'NumberOfInstancesWithMissingValues': 709.0,
'NumberOfMissingValues': 8199.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 45... | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | [
"id_arbre",
"commune",
"quartier",
"site",
"cote_voirie",
"matricule_arbre",
"genre_arbre",
"espece_arbre",
"controle",
"situation",
"type_sol",
"surf_permeable",
"date_plantation",
"classe_age",
"hauteur",
"classe_hauteur",
"diametre",
"circonference-en-cm",
"classe_circonferenc... | [
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,... | 2,957 |
362,406 | predictive_accuracy | accuracy_score | ibm-employee-performance | IBM Employee Attrition Data
The dataset used in the code pattern is supplied by Kaggle and contains HR analytics data of employees
that stay and leave. The types of data include metrics such as education level, job satisfactions, and commmute distance.
The dataset was obtained
from https://github.com/IBM/employee-a... | {0: [0 - Age (numeric)],
1: [1 - BusinessTravel (string)],
2: [2 - DailyRate (numeric)],
3: [3 - Department (string)],
4: [4 - DistanceFromHome (numeric)],
5: [5 - Education (numeric)],
6: [6 - EducationField (string)],
7: [7 - EmployeeCount (numeric)],
8: [8 - EmployeeNumber (numeric)],
9: [9 - EnvironmentSat... | {'MajorityClassSize': 1244.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 226.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 34.0,
'NumberOfInstances': 1470.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,... | ibm-employee-performance | [
"Age",
"BusinessTravel",
"DailyRate",
"Department",
"DistanceFromHome",
"Education",
"EducationField",
"EmployeeCount",
"EmployeeNumber",
"EnvironmentSatisfaction",
"Gender",
"HourlyRate",
"JobInvolvement",
"JobLevel",
"JobRole",
"JobSatisfaction",
"MaritalStatus",
"MonthlyIncome",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,958 |
362,408 | predictive_accuracy | accuracy_score | FakeMovie | A fake movie dataset. | {0: [0 - rate_ave_1 (numeric)],
1: [1 - rate_ave_2 (numeric)],
2: [2 - type (nominal)],
3: [3 - seen (nominal)]} | {'MajorityClassSize': 14.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 14.0,
'NumberOfClasses': 1.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | FakeMovie | [
"rate_ave_1",
"rate_ave_2",
"type"
] | [
false,
false,
true
] | 2,959 |
362,576 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
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true,
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true,
true,
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true,
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true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,960 |
362,439 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 2,961 |
362,444 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 2,962 |
362,307 | predictive_accuracy | accuracy_score | dgf_test | dgf_test | {0: [0 - date (string)],
1: [1 - date_annees (numeric)],
2: [2 - type-de-ligne (string)],
3: [3 - nom-de-la-ligne (string)],
4: [4 - nombre-de-voyages (numeric)]} | {'MajorityClassSize': 2851.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 564.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 3415.0,
'NumberOfInstancesWithMissingValues': 1.0,
'NumberOfMissingValues': 1.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
... | dgf_test | [
"date",
"date_annees",
"nom-de-la-ligne",
"nombre-de-voyages"
] | [
false,
false,
false,
false
] | 2,963 |
362,581 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
true,
true,
false,
false,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,964 |
168,327 | predictive_accuracy | accuracy_score | enron | Multi-label dataset. The UC Berkeley enron4 dataset represents a subset of the original enron5 dataset and consists of 1684 cases of emails with 21 labels and 1001 predictor variables. | {0: [0 - X0 (nominal)],
1: [1 - X00 (nominal)],
2: [2 - X000 (nominal)],
3: [3 - X01 (nominal)],
4: [4 - X02 (nominal)],
5: [5 - X03 (nominal)],
6: [6 - X04 (nominal)],
7: [7 - X05 (nominal)],
8: [8 - X06 (nominal)],
9: [9 - X07 (nominal)],
10: [10 - X08 (nominal)],
11: [11 - X09 (nominal)],
12: [12 - X1 (n... | {'MajorityClassSize': 1676.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 26.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1054.0,
'NumberOfInstances': 1702.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 1054... | enron | [
"X0",
"X00",
"X000",
"X01",
"X02",
"X03",
"X04",
"X05",
"X06",
"X07",
"X08",
"X09",
"X1",
"X10",
"X100",
"X11",
"X12",
"X13",
"X14",
"X15",
"X16",
"X17",
"X18",
"X19",
"X1999",
"X2",
"X20",
"X200",
"X2000",
"X2001",
"X2002",
"X20a",
"X20and",
"X20as"... | [
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 2,966 |
362,412 | predictive_accuracy | accuracy_score | car_evaluation | Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX, M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.). In this version duplicate rows are dropped | {0: [0 - buying (nominal)],
1: [1 - maint (nominal)],
2: [2 - doors (nominal)],
3: [3 - persons (nominal)],
4: [4 - lug_boot (nominal)],
5: [5 - safety (nominal)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 1210.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 65.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1728.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'... | car_evaluation | [
"buying",
"maint",
"doors",
"persons",
"lug_boot",
"safety"
] | [
true,
true,
true,
true,
true,
true
] | 2,967 |
362,582 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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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true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,968 |
362,578 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
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true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,969 |
362,577 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,970 |
362,584 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
true,
true,
false,
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true,
true,
true,
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true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,971 |
362,575 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
true,
true,
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true,
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true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,973 |
362,583 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
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true,
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true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,974 |
362,550 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 2,975 |
362,551 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 2,976 |
362,542 | predictive_accuracy | accuracy_score | Heart_disease_prediction_20 | This database contains 14 attributes. The goal field refers to the presence of heart disease in the patient. It is integer valued with 0 or 1. | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - cp (numeric)],
3: [3 - trestpbs (numeric)],
4: [4 - chol (numeric)],
5: [5 - fbs (numeric)],
6: [6 - restecg (numeric)],
7: [7 - thalach (numeric)],
8: [8 - exang (numeric)],
9: [9 - oldpeak (numeric)],
10: [10 - slope (numeric)],
11: [11 - ca (string)... | {'MajorityClassSize': 159.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 137.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 296.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0,
... | Heart_disease_prediction_20 | [
"age",
"sex",
"cp",
"trestpbs",
"chol",
"fbs",
"restecg",
"thalach",
"exang",
"oldpeak",
"slope",
"ca",
"thal"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,977 |
362,586 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
true,
true,
false,
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true,
true,
true,
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true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,978 |
362,579 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,979 |
362,540 | predictive_accuracy | accuracy_score | phoneme | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
**Author**: Dominique Van Cappel, THOMSON-SINTRA
**Source**: [KEEL](http://sci2s.ugr.es/keel/dataset.... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - Class (nominal)]} | {'MajorityClassSize': 1586.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1586.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 3172.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
... | phoneme | [
"V1",
"V2",
"V3",
"V4",
"V5"
] | [
false,
false,
false,
false,
false
] | 2,980 |
362,350 | predictive_accuracy | accuracy_score | mini_insect_1 | mini insect example dataset # 1 | {0: [0 - Data (string)],
1: [1 - Shape (string)],
2: [2 - Label (string)],
3: [3 - SuperCategory (string)]} | {'MajorityClassSize': 3.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 12.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | mini_insect_1 | [
"Data",
"Shape",
"SuperCategory"
] | [
false,
false,
false
] | 2,981 |
362,554 | predictive_accuracy | accuracy_score | AutoMLSelectorMulticlass | A multiclass classification problem to predict the best AutoML tool to solve a given SL task. | {0: [0 - AutoCorrelation (numeric)],
1: [1 - CfsSubsetEval_DecisionStumpAUC (numeric)],
2: [2 - CfsSubsetEval_DecisionStumpErrRate (numeric)],
3: [3 - CfsSubsetEval_DecisionStumpKappa (numeric)],
4: [4 - CfsSubsetEval_NaiveBayesAUC (numeric)],
5: [5 - CfsSubsetEval_NaiveBayesErrRate (numeric)],
6: [6 - CfsSubsetE... | {'MajorityClassSize': 53.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 108.0,
'NumberOfInstances': 103.0,
'NumberOfInstancesWithMissingValues': 82.0,
'NumberOfMissingValues': 3766.0,
'NumberOfNumericFeatures': 107.0,
'NumberOfSymbolicFeatures': 1.0... | AutoMLSelectorMulticlass | [
"AutoCorrelation",
"CfsSubsetEval_DecisionStumpAUC",
"CfsSubsetEval_DecisionStumpErrRate",
"CfsSubsetEval_DecisionStumpKappa",
"CfsSubsetEval_NaiveBayesAUC",
"CfsSubsetEval_NaiveBayesErrRate",
"CfsSubsetEval_NaiveBayesKappa",
"CfsSubsetEval_kNN1NAUC",
"CfsSubsetEval_kNN1NErrRate",
"CfsSubsetEval_k... | [
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... | 2,982 |
362,349 | predictive_accuracy | accuracy_score | Birds | MINI - Birds dataset | {0: [0 - Data (string)], 1: [1 - Shape (string)], 2: [2 - Label (string)]} | {'MajorityClassSize': 25.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 25.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Birds | [
"Data",
"Shape"
] | [
false,
false
] | 2,983 |
362,459 | predictive_accuracy | accuracy_score | wine | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.
Original source: https://archive.ics.uci.edu/ml/datasets/wine+quality Please give credit to the original source if you u... | {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': 1277.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1277.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2554.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0... | wine | [
"fixed acidity",
"volatile acidity",
"citric acid",
"residual sugar",
"chlorides",
"free sulfur dioxide",
"total sulfur dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,984 |
362,528 | predictive_accuracy | accuracy_score | phoneme | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
**Author**: Dominique Van Cappel, THOMSON-SINTRA
**Source**: [KEEL](http://sci2s.ugr.es/keel/dataset.... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - Class (nominal)]} | {'MajorityClassSize': 1586.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1586.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 3172.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
... | phoneme | [
"V1",
"V2",
"V3",
"V4",
"V5"
] | [
false,
false,
false,
false,
false
] | 2,985 |
362,391 | predictive_accuracy | accuracy_score | Zombies-Apocalypse | Context
News reports suggest that the impossible has become possiblezombies have appeared on the streets of the US! What should we do? The Centers for Disease Control and Prevention (CDC) zombie preparedness website recommends storing water, food, medication, tools, sanitation items, clothing, essential documents, and ... | {0: [0 - zombieid (numeric)],
1: [1 - zombie (string)],
2: [2 - age (numeric)],
3: [3 - sex (string)],
4: [4 - rurality (string)],
5: [5 - household (numeric)],
6: [6 - water (numeric)],
7: [7 - food (string)],
8: [8 - medication (string)],
9: [9 - tools (string)],
10: [10 - firstaid (string)],
11: [11 - san... | {'MajorityClassSize': 121.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 79.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 200.0,
'NumberOfInstancesWithMissingValues': 146.0,
'NumberOfMissingValues': 208.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,... | Zombies-Apocalypse | [
"age",
"sex",
"rurality",
"household",
"water",
"food",
"medication",
"tools",
"firstaid",
"sanitation",
"clothing",
"documents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,986 |
362,580 | predictive_accuracy | accuracy_score | anneal | **Author**: donated by David Sterling and Wray Buntine
**Source**: [original (UCI)](http://www.openml.org/d/2) -
**Please cite**:
This is a preprocessed version of the <a href="d/2">anneal</a> dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because t... | {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': 486.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 412.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
... | 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",
"... | [
true,
true,
true,
false,
false,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 2,988 |
362,541 | predictive_accuracy | accuracy_score | vehicle_reproduced | **Author**: Dr. Pete Mowforth and Dr. Barry Shepherd
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Vehicle+Silhouettes))
**Please cite**: Siebert,JP. Turing Institute Research Memorandum TIRM-87-018 "Vehicle Recognition Using Rule Based Methods" (March 1987)
NAME
vehicle silhoue... | {0: [0 - COMPACTNESS (numeric)],
1: [1 - CIRCULARITY (numeric)],
2: [2 - DISTANCE_CIRCULARITY (numeric)],
3: [3 - RADIUS_RATIO (numeric)],
4: [4 - PR.AXIS_ASPECT_RATIO (numeric)],
5: [5 - MAX.LENGTH_ASPECT_RATIO (numeric)],
6: [6 - SCATTER_RATIO (numeric)],
7: [7 - ELONGATEDNESS (numeric)],
8: [8 - PR.AXIS_RECT... | {'MajorityClassSize': 218.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 199.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 846.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 1.0,
... | vehicle_reproduced | [
"COMPACTNESS",
"CIRCULARITY",
"DISTANCE_CIRCULARITY",
"RADIUS_RATIO",
"PR.AXIS_ASPECT_RATIO",
"MAX.LENGTH_ASPECT_RATIO",
"SCATTER_RATIO",
"ELONGATEDNESS",
"PR.AXIS_RECTANGULARITY",
"MAX.LENGTH_RECTANGULARITY",
"SCALED_VARIANCE_MAJOR",
"SCALED_VARIANCE_MINOR",
"SCALED_RADIUS_OF_GYRATION",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,989 |
362,377 | predictive_accuracy | accuracy_score | Loan-Predication | Among all industries, insurance domain has the largest use of analytics data science methods. This data set would provide you enough taste of working on data sets from insurance companies, what challenges are faced, what strategies are used, which variables influence the outcome etc. This is a classification problem. ... | {0: [0 - Loan_ID (string)],
1: [1 - Gender (string)],
2: [2 - Married (string)],
3: [3 - Dependents (string)],
4: [4 - Education (string)],
5: [5 - Self_Employed (string)],
6: [6 - ApplicantIncome (numeric)],
7: [7 - CoapplicantIncome (numeric)],
8: [8 - LoanAmount (numeric)],
9: [9 - Loan_Amount_Term (numeric... | {'MajorityClassSize': 422.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 192.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 614.0,
'NumberOfInstancesWithMissingValues': 134.0,
'NumberOfMissingValues': 149.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0... | Loan-Predication | [
"Gender",
"Married",
"Dependents",
"Education",
"Self_Employed",
"ApplicantIncome",
"CoapplicantIncome",
"LoanAmount",
"Loan_Amount_Term",
"Credit_History",
"Property_Area"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,990 |
362,388 | predictive_accuracy | accuracy_score | Toronto-Apartment-Rental-Price | Context
I have collected the Toronto Apartment Rental prices from various sources in local websites.
Content
There are 7 columns in the dataset.
Bedroom - How many bedrooms available
Bathroom - How many bathrooms available
Den - Whether den is available or not
Address - Location
Lat - Lattitude
Long - Longitude
Price ... | {0: [0 - Bedroom (numeric)],
1: [1 - Bathroom (numeric)],
2: [2 - Den (numeric)],
3: [3 - Address (string)],
4: [4 - Lat (numeric)],
5: [5 - Long (numeric)],
6: [6 - Price (string)]} | {'MajorityClassSize': 58.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 188.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1124.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | Toronto-Apartment-Rental-Price | [
"Bedroom",
"Bathroom",
"Den",
"Address",
"Lat",
"Long"
] | [
false,
false,
false,
false,
false,
false
] | 2,991 |
362,597 | predictive_accuracy | accuracy_score | credit_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,993 |
362,602 | predictive_accuracy | accuracy_score | credit_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,994 |
3,930 | predictive_accuracy | accuracy_score | mouseType | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data from the RSCTC 2010 Discovery Challenge. All datasets contain between 100 and 400 samples, characterized by values of 20,000 - 65,000 attributes. Samples are assigned to several (2-10) classes. All attributes are numeric and represent measure... | {0: [0 - Var1 (numeric)],
1: [1 - Var2 (numeric)],
2: [2 - Var3 (numeric)],
3: [3 - Var4 (numeric)],
4: [4 - Var5 (numeric)],
5: [5 - Var6 (numeric)],
6: [6 - Var7 (numeric)],
7: [7 - Var8 (numeric)],
8: [8 - Var9 (numeric)],
9: [9 - Var10 (numeric)],
10: [10 - Var11 (numeric)],
11: [11 - Var12 (numeric)],
... | {'MajorityClassSize': 69.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 13.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 45102.0,
'NumberOfInstances': 214.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 45101.0,
'NumberOfSymbolicFeatures': 1.... | mouseType | [
"Var1",
"Var2",
"Var3",
"Var4",
"Var5",
"Var6",
"Var7",
"Var8",
"Var9",
"Var10",
"Var11",
"Var12",
"Var13",
"Var14",
"Var15",
"Var16",
"Var17",
"Var18",
"Var19",
"Var20",
"Var21",
"Var22",
"Var23",
"Var24",
"Var25",
"Var26",
"Var27",
"Var28",
"Var29",
"Var30... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,997 |
362,596 | predictive_accuracy | accuracy_score | credit_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,998 |
362,601 | predictive_accuracy | accuracy_score | credit_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,000 |
362,595 | predictive_accuracy | accuracy_score | credit_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,001 |
362,591 | predictive_accuracy | accuracy_score | credit_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,002 |
362,569 | predictive_accuracy | accuracy_score | UCI_churn | No description available | {0: [0 - state (string)],
1: [1 - account length (numeric)],
2: [2 - area code (numeric)],
3: [3 - phone number (string)],
4: [4 - international plan (string)],
5: [5 - voice mail plan (string)],
6: [6 - number vmail messages (numeric)],
7: [7 - total day minutes (numeric)],
8: [8 - total day calls (numeric)],
... | {'MajorityClassSize': 2850.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 483.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 3333.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 1.0,... | UCI_churn | [
"state",
"account length",
"area code",
"phone number",
"international plan",
"voice mail plan",
"number vmail messages",
"total day minutes",
"total day calls",
"total day charge",
"total eve minutes",
"total eve calls",
"total eve charge",
"total night minutes",
"total night calls",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,003 |
362,599 | predictive_accuracy | accuracy_score | credit_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,004 |
362,604 | predictive_accuracy | accuracy_score | credit_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,005 |
362,600 | predictive_accuracy | accuracy_score | credit_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,006 |
362,404 | predictive_accuracy | accuracy_score | ibm-employee-attrition | IBM Employee Attrition Data
The dataset used in the code pattern is supplied by Kaggle and contains HR analytics data of employees
that stay and leave. The types of data include metrics such as education level, job satisfactions, and commmute distance.
The dataset was obtained
from https://github.com/IBM/employee-a... | {0: [0 - Age (numeric)],
1: [1 - Attrition (string)],
2: [2 - BusinessTravel (string)],
3: [3 - DailyRate (numeric)],
4: [4 - Department (string)],
5: [5 - DistanceFromHome (numeric)],
6: [6 - Education (numeric)],
7: [7 - EducationField (string)],
8: [8 - EmployeeCount (numeric)],
9: [9 - EmployeeNumber (nume... | {'MajorityClassSize': 1233.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 237.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 35.0,
'NumberOfInstances': 1470.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 0.0,... | ibm-employee-attrition | [
"Age",
"BusinessTravel",
"DailyRate",
"Department",
"DistanceFromHome",
"Education",
"EducationField",
"EmployeeCount",
"EmployeeNumber",
"EnvironmentSatisfaction",
"Gender",
"HourlyRate",
"JobInvolvement",
"JobLevel",
"JobRole",
"JobSatisfaction",
"MaritalStatus",
"MonthlyIncome",... | [
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
] | 3,007 |
362,593 | predictive_accuracy | accuracy_score | credit_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,009 |
362,405 | predictive_accuracy | accuracy_score | ibm-employee-attrition | IBM Employee Attrition Data
The dataset used in the code pattern is supplied by Kaggle and contains HR analytics data of employees
that stay and leave. The types of data include metrics such as education level, job satisfactions, and commmute distance.
The dataset was obtained
from https://github.com/IBM/employee-a... | {0: [0 - Age (numeric)],
1: [1 - Attrition (string)],
2: [2 - BusinessTravel (string)],
3: [3 - DailyRate (numeric)],
4: [4 - Department (string)],
5: [5 - DistanceFromHome (numeric)],
6: [6 - Education (numeric)],
7: [7 - EducationField (string)],
8: [8 - EmployeeCount (numeric)],
9: [9 - EmployeeNumber (nume... | {'MajorityClassSize': 1233.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 237.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 35.0,
'NumberOfInstances': 1470.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 0.0,... | ibm-employee-attrition | [
"Age",
"BusinessTravel",
"DailyRate",
"Department",
"DistanceFromHome",
"Education",
"EducationField",
"EmployeeCount",
"EmployeeNumber",
"EnvironmentSatisfaction",
"Gender",
"HourlyRate",
"JobInvolvement",
"JobLevel",
"JobRole",
"JobSatisfaction",
"MaritalStatus",
"MonthlyIncome",... | [
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
] | 3,011 |
362,606 | predictive_accuracy | accuracy_score | credit_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,012 |
362,530 | predictive_accuracy | accuracy_score | credit-g_reproduced_1 | **Author**: Dr. Hans Hofmann
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**German Credit dataset**
This dataset classifies people described by a set of attributes as good or bad credit... | {0: [0 - checking_status (nominal)],
1: [1 - duration (numeric)],
2: [2 - credit_history (nominal)],
3: [3 - purpose (nominal)],
4: [4 - credit_amount (numeric)],
5: [5 - savings_status (nominal)],
6: [6 - employment (nominal)],
7: [7 - installment_commitment (numeric)],
8: [8 - personal_status (nominal)],
9: ... | {'MajorityClassSize': 700.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 300.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 14.0,
... | credit-g_reproduced_1 | [
"checking_status",
"duration",
"credit_history",
"purpose",
"credit_amount",
"savings_status",
"employment",
"installment_commitment",
"personal_status",
"other_parties",
"residence_since",
"property_magnitude",
"age",
"other_payment_plans",
"housing",
"existing_credits",
"job",
"n... | [
true,
false,
true,
true,
false,
true,
true,
false,
true,
true,
false,
true,
false,
true,
true,
false,
true,
false,
true,
true
] | 3,013 |
362,608 | predictive_accuracy | accuracy_score | california_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset california (44090) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - MedInc (numeric)],
1: [1 - HouseAge (numeric)],
2: [2 - AveRooms (numeric)],
3: [3 - AveBedrms (numeric)],
4: [4 - Population (numeric)],
5: [5 - AveOccup (numeric)],
6: [6 - Latitude (numeric)],
7: [7 - Longitude (numeric)],
8: [8 - price (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
... | california_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"MedInc",
"HouseAge",
"AveRooms",
"AveBedrms",
"Population",
"AveOccup",
"Latitude",
"Longitude"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 3,014 |
361,331 | predictive_accuracy | accuracy_score | GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_EDM-1_1 | GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_EDM-1_1-pmlb | {0: [0 - N0 (nominal)],
1: [1 - N1 (nominal)],
2: [2 - N2 (nominal)],
3: [3 - N3 (nominal)],
4: [4 - N4 (nominal)],
5: [5 - N5 (nominal)],
6: [6 - N6 (nominal)],
7: [7 - N7 (nominal)],
8: [8 - N8 (nominal)],
9: [9 - N9 (nominal)],
10: [10 - N10 (nominal)],
11: [11 - N11 (nominal)],
12: [12 - N12 (nominal)],... | {'MajorityClassSize': 800.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 800.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1001.0,
'NumberOfInstances': 1600.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 1001... | GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_EDM-1_1 | [
"N0",
"N1",
"N2",
"N3",
"N4",
"N5",
"N6",
"N7",
"N8",
"N9",
"N10",
"N11",
"N12",
"N13",
"N14",
"N15",
"N16",
"N17",
"N18",
"N19",
"N20",
"N21",
"N22",
"N23",
"N24",
"N25",
"N26",
"N27",
"N28",
"N29",
"N30",
"N31",
"N32",
"N33",
"N34",
"N35",
"N... | [
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... | 3,015 |
362,544 | predictive_accuracy | accuracy_score | Iris | 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 referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. ... | {0: [0 - sepal_length (numeric)],
1: [1 - sepal_width (numeric)],
2: [2 - petal_length (numeric)],
3: [3 - petal_width (numeric)],
4: [4 - class (string)]} | {'MajorityClassSize': 50.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 49.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 149.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Iris | [
"sepal_length",
"sepal_width",
"petal_length",
"petal_width"
] | [
false,
false,
false,
false
] | 3,017 |
362,306 | predictive_accuracy | accuracy_score | dgf_test | dgf_test | {0: [0 - date (string)],
1: [1 - date_annees (numeric)],
2: [2 - type-de-ligne (string)],
3: [3 - nom-de-la-ligne (string)],
4: [4 - nombre-de-voyages (numeric)]} | {'MajorityClassSize': 2851.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 564.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 3415.0,
'NumberOfInstancesWithMissingValues': 1.0,
'NumberOfMissingValues': 1.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
... | dgf_test | [
"date",
"date_annees",
"nom-de-la-ligne",
"nombre-de-voyages"
] | [
false,
false,
false,
false
] | 3,018 |
362,605 | predictive_accuracy | accuracy_score | credit_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit (44089) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - RevolvingUtilizationOfUnsecuredLines (numeric)],
1: [1 - age (numeric)],
2: [2 - NumberOfTime30-59DaysPastDueNotWorse (numeric)],
3: [3 - DebtRatio (numeric)],
4: [4 - MonthlyIncome (numeric)],
5: [5 - NumberOfOpenCreditLinesAndLoans (numeric)],
6: [6 - NumberOfTimes90DaysLate (numeric)],
7: [7 - Number... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | credit_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RevolvingUtilizationOfUnsecuredLines",
"age",
"NumberOfTime30-59DaysPastDueNotWorse",
"DebtRatio",
"MonthlyIncome",
"NumberOfOpenCreditLinesAndLoans",
"NumberOfTimes90DaysLate",
"NumberRealEstateLoansOrLines",
"NumberOfTime60-89DaysPastDueNotWorse",
"NumberOfDependents"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,019 |
362,614 | predictive_accuracy | accuracy_score | wine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset wine (44091) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - 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': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0... | wine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"fixed acidity",
"volatile acidity",
"citric acid",
"residual sugar",
"chlorides",
"free sulfur dioxide",
"total sulfur dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,020 |
362,613 | predictive_accuracy | accuracy_score | wine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset wine (44091) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - 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': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0... | wine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"fixed acidity",
"volatile acidity",
"citric acid",
"residual sugar",
"chlorides",
"free sulfur dioxide",
"total sulfur dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,021 |
362,610 | predictive_accuracy | accuracy_score | california_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset california (44090) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - MedInc (numeric)],
1: [1 - HouseAge (numeric)],
2: [2 - AveRooms (numeric)],
3: [3 - AveBedrms (numeric)],
4: [4 - Population (numeric)],
5: [5 - AveOccup (numeric)],
6: [6 - Latitude (numeric)],
7: [7 - Longitude (numeric)],
8: [8 - price (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
... | california_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"MedInc",
"HouseAge",
"AveRooms",
"AveBedrms",
"Population",
"AveOccup",
"Latitude",
"Longitude"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 3,022 |
4,633 | predictive_accuracy | accuracy_score | rsctc2010_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data from the RSCTC 2010 Discovery Challenge. Example datasets for 6 different problems of DNA microarray data analysis and classification. All datasets contain gene expression data characterized by values of 20,000 - 65,000 attributes. Samples ar... | {0: [0 - Var1 (numeric)],
1: [1 - Var2 (numeric)],
2: [2 - Var3 (numeric)],
3: [3 - Var4 (numeric)],
4: [4 - Var5 (numeric)],
5: [5 - Var6 (numeric)],
6: [6 - Var7 (numeric)],
7: [7 - Var8 (numeric)],
8: [8 - Var9 (numeric)],
9: [9 - Var10 (numeric)],
10: [10 - Var11 (numeric)],
11: [11 - Var12 (numeric)],
... | {'MajorityClassSize': 43.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 10.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 54614.0,
'NumberOfInstances': 89.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 54613.0,
'NumberOfSymbolicFeatures': 1.0... | rsctc2010_5 | [
"Var1",
"Var2",
"Var3",
"Var4",
"Var5",
"Var6",
"Var7",
"Var8",
"Var9",
"Var10",
"Var11",
"Var12",
"Var13",
"Var14",
"Var15",
"Var16",
"Var17",
"Var18",
"Var19",
"Var20",
"Var21",
"Var22",
"Var23",
"Var24",
"Var25",
"Var26",
"Var27",
"Var28",
"Var29",
"Var30... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,023 |
362,536 | predictive_accuracy | accuracy_score | kdd_ipums_la_97-small | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the origi... | {0: [0 - value (numeric)],
1: [1 - rent (numeric)],
2: [2 - ftotinc (numeric)],
3: [3 - momloc (numeric)],
4: [4 - famsize (numeric)],
5: [5 - nchild (numeric)],
6: [6 - eldch (numeric)],
7: [7 - yngch (numeric)],
8: [8 - nsibs (numeric)],
9: [9 - age (numeric)],
10: [10 - occscore (numeric)],
11: [11 - sei ... | {'MajorityClassSize': 2594.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2594.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 5188.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 1.0... | kdd_ipums_la_97-small | [
"value",
"rent",
"ftotinc",
"momloc",
"famsize",
"nchild",
"eldch",
"yngch",
"nsibs",
"age",
"occscore",
"sei",
"inctot",
"incwage",
"incbus",
"incfarm",
"incss",
"incwelfr",
"incother",
"poverty"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,024 |
362,611 | predictive_accuracy | accuracy_score | california_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset california (44090) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - MedInc (numeric)],
1: [1 - HouseAge (numeric)],
2: [2 - AveRooms (numeric)],
3: [3 - AveBedrms (numeric)],
4: [4 - Population (numeric)],
5: [5 - AveOccup (numeric)],
6: [6 - Latitude (numeric)],
7: [7 - Longitude (numeric)],
8: [8 - price (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
... | california_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"MedInc",
"HouseAge",
"AveRooms",
"AveBedrms",
"Population",
"AveOccup",
"Latitude",
"Longitude"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 3,025 |
362,612 | predictive_accuracy | accuracy_score | wine_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset wine (44091) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - 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': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0... | wine_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"fixed acidity",
"volatile acidity",
"citric acid",
"residual sugar",
"chlorides",
"free sulfur dioxide",
"total sulfur dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,026 |
362,619 | predictive_accuracy | accuracy_score | electricity_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset electricity (44120) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: ... | {0: [0 - date (numeric)],
1: [1 - period (numeric)],
2: [2 - nswprice (numeric)],
3: [3 - nswdemand (numeric)],
4: [4 - vicprice (numeric)],
5: [5 - vicdemand (numeric)],
6: [6 - transfer (numeric)],
7: [7 - class (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
... | electricity_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"date",
"period",
"nswprice",
"nswdemand",
"vicprice",
"vicdemand",
"transfer"
] | [
false,
false,
false,
false,
false,
false,
false
] | 3,027 |
362,430 | predictive_accuracy | accuracy_score | rl | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "classification on categorical and
numerical features" benchmark. Original description:
The goal of this challenge is... | {0: [0 - V1 (numeric)],
1: [1 - V5 (numeric)],
2: [2 - V6 (numeric)],
3: [3 - V8 (nominal)],
4: [4 - V14 (nominal)],
5: [5 - V15 (nominal)],
6: [6 - V17 (nominal)],
7: [7 - V18 (nominal)],
8: [8 - V19 (nominal)],
9: [9 - V20 (numeric)],
10: [10 - V21 (numeric)],
11: [11 - V22 (nominal)],
12: [12 - class (st... | {'MajorityClassSize': 2485.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2485.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 4970.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 7.0,... | rl | [
"V1",
"V5",
"V6",
"V8",
"V14",
"V15",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22"
] | [
false,
false,
false,
true,
true,
true,
true,
true,
true,
false,
false,
true
] | 3,028 |
362,618 | predictive_accuracy | accuracy_score | electricity_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset electricity (44120) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: ... | {0: [0 - date (numeric)],
1: [1 - period (numeric)],
2: [2 - nswprice (numeric)],
3: [3 - nswdemand (numeric)],
4: [4 - vicprice (numeric)],
5: [5 - vicdemand (numeric)],
6: [6 - transfer (numeric)],
7: [7 - class (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
... | electricity_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"date",
"period",
"nswprice",
"nswdemand",
"vicprice",
"vicdemand",
"transfer"
] | [
false,
false,
false,
false,
false,
false,
false
] | 3,029 |
362,617 | predictive_accuracy | accuracy_score | electricity_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset electricity (44120) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: ... | {0: [0 - date (numeric)],
1: [1 - period (numeric)],
2: [2 - nswprice (numeric)],
3: [3 - nswdemand (numeric)],
4: [4 - vicprice (numeric)],
5: [5 - vicdemand (numeric)],
6: [6 - transfer (numeric)],
7: [7 - class (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
... | electricity_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"date",
"period",
"nswprice",
"nswdemand",
"vicprice",
"vicdemand",
"transfer"
] | [
false,
false,
false,
false,
false,
false,
false
] | 3,030 |
362,616 | predictive_accuracy | accuracy_score | wine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset wine (44091) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - 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': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0... | wine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"fixed acidity",
"volatile acidity",
"citric acid",
"residual sugar",
"chlorides",
"free sulfur dioxide",
"total sulfur dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,031 |
362,615 | predictive_accuracy | accuracy_score | wine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset wine (44091) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - 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': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0... | wine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"fixed acidity",
"volatile acidity",
"citric acid",
"residual sugar",
"chlorides",
"free sulfur dioxide",
"total sulfur dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,032 |
362,452 | predictive_accuracy | accuracy_score | phoneme | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original description:
**Author**: Dominique Van Cappel, THOMSON-SINTRA
**Source**: [KEEL](http://sci2s.ugr.es/keel/data... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - Class (string)]} | {'MajorityClassSize': 1586.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1586.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 3172.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
... | phoneme | [
"V1",
"V2",
"V3",
"V4",
"V5"
] | [
false,
false,
false,
false,
false
] | 3,033 |
362,620 | predictive_accuracy | accuracy_score | electricity_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset electricity (44120) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: ... | {0: [0 - date (numeric)],
1: [1 - period (numeric)],
2: [2 - nswprice (numeric)],
3: [3 - nswdemand (numeric)],
4: [4 - vicprice (numeric)],
5: [5 - vicdemand (numeric)],
6: [6 - transfer (numeric)],
7: [7 - class (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
... | electricity_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"date",
"period",
"nswprice",
"nswdemand",
"vicprice",
"vicdemand",
"transfer"
] | [
false,
false,
false,
false,
false,
false,
false
] | 3,034 |
362,621 | predictive_accuracy | accuracy_score | electricity_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset electricity (44120) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: ... | {0: [0 - date (numeric)],
1: [1 - period (numeric)],
2: [2 - nswprice (numeric)],
3: [3 - nswdemand (numeric)],
4: [4 - vicprice (numeric)],
5: [5 - vicdemand (numeric)],
6: [6 - transfer (numeric)],
7: [7 - class (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
... | electricity_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"date",
"period",
"nswprice",
"nswdemand",
"vicprice",
"vicdemand",
"transfer"
] | [
false,
false,
false,
false,
false,
false,
false
] | 3,035 |
362,506 | predictive_accuracy | accuracy_score | rl | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "classification on categorical and
numerical features" benchmark. Original description:
The goal of this challenge is... | {0: [0 - V1 (numeric)],
1: [1 - V5 (numeric)],
2: [2 - V6 (numeric)],
3: [3 - V8 (nominal)],
4: [4 - V14 (nominal)],
5: [5 - V15 (nominal)],
6: [6 - V17 (nominal)],
7: [7 - V18 (nominal)],
8: [8 - V19 (nominal)],
9: [9 - V20 (numeric)],
10: [10 - V21 (numeric)],
11: [11 - V22 (nominal)],
12: [12 - class (st... | {'MajorityClassSize': 2485.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2485.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 4970.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 7.0,... | rl | [
"V1",
"V5",
"V6",
"V8",
"V14",
"V15",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22"
] | [
false,
false,
false,
true,
true,
true,
true,
true,
true,
false,
false,
true
] | 3,036 |
362,627 | predictive_accuracy | accuracy_score | pol_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset pol (44122) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 10... | {0: [0 - f5 (numeric)],
1: [1 - f6 (numeric)],
2: [2 - f7 (numeric)],
3: [3 - f8 (numeric)],
4: [4 - f9 (numeric)],
5: [5 - f13 (numeric)],
6: [6 - f14 (numeric)],
7: [7 - f15 (numeric)],
8: [8 - f16 (numeric)],
9: [9 - f17 (numeric)],
10: [10 - f18 (numeric)],
11: [11 - f19 (numeric)],
12: [12 - f20 (numer... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 27.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 1.0... | pol_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"f5",
"f6",
"f7",
"f8",
"f9",
"f13",
"f14",
"f15",
"f16",
"f17",
"f18",
"f19",
"f20",
"f21",
"f22",
"f23",
"f24",
"f25",
"f26",
"f27",
"f28",
"f29",
"f30",
"f31",
"f32",
"f33"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,037 |
362,563 | predictive_accuracy | accuracy_score | South_Asian_Churn_dataset | The SATO data set used is real life data collected from a major wireless telecom operator in South Asia. | {0: [0 - network_age (numeric)],
1: [1 - Aggregate_Total_Rev (numeric)],
2: [2 - Aggregate_SMS_Rev (numeric)],
3: [3 - Aggregate_Data_Rev (numeric)],
4: [4 - Aggregate_Data_Vol (numeric)],
5: [5 - Aggregate_Calls (numeric)],
6: [6 - Aggregate_ONNET_REV (numeric)],
7: [7 - Aggregate_OFFNET_REV (numeric)],
8: [8 ... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 279.0,
'NumberOfMissingValues': 453.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': ... | South_Asian_Churn_dataset | [
"network_age",
"Aggregate_Total_Rev",
"Aggregate_SMS_Rev",
"Aggregate_Data_Rev",
"Aggregate_Data_Vol",
"Aggregate_Calls",
"Aggregate_ONNET_REV",
"Aggregate_OFFNET_REV",
"Aggregate_complaint_count",
"aug_user_type",
"sep_user_type",
"aug_fav_a",
"sep_fav_a"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,038 |
362,628 | predictive_accuracy | accuracy_score | pol_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset pol (44122) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 10... | {0: [0 - f5 (numeric)],
1: [1 - f6 (numeric)],
2: [2 - f7 (numeric)],
3: [3 - f8 (numeric)],
4: [4 - f9 (numeric)],
5: [5 - f13 (numeric)],
6: [6 - f14 (numeric)],
7: [7 - f15 (numeric)],
8: [8 - f16 (numeric)],
9: [9 - f17 (numeric)],
10: [10 - f18 (numeric)],
11: [11 - f19 (numeric)],
12: [12 - f20 (numer... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 27.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 1.0... | pol_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"f5",
"f6",
"f7",
"f8",
"f9",
"f13",
"f14",
"f15",
"f16",
"f17",
"f18",
"f19",
"f20",
"f21",
"f22",
"f23",
"f24",
"f25",
"f26",
"f27",
"f28",
"f29",
"f30",
"f31",
"f32",
"f33"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,039 |
362,631 | predictive_accuracy | accuracy_score | pol_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset pol (44122) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 10... | {0: [0 - f5 (numeric)],
1: [1 - f6 (numeric)],
2: [2 - f7 (numeric)],
3: [3 - f8 (numeric)],
4: [4 - f9 (numeric)],
5: [5 - f13 (numeric)],
6: [6 - f14 (numeric)],
7: [7 - f15 (numeric)],
8: [8 - f16 (numeric)],
9: [9 - f17 (numeric)],
10: [10 - f18 (numeric)],
11: [11 - f19 (numeric)],
12: [12 - f20 (numer... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 27.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 1.0... | pol_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"f5",
"f6",
"f7",
"f8",
"f9",
"f13",
"f14",
"f15",
"f16",
"f17",
"f18",
"f19",
"f20",
"f21",
"f22",
"f23",
"f24",
"f25",
"f26",
"f27",
"f28",
"f29",
"f30",
"f31",
"f32",
"f33"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,040 |
362,629 | predictive_accuracy | accuracy_score | pol_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset pol (44122) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 10... | {0: [0 - f5 (numeric)],
1: [1 - f6 (numeric)],
2: [2 - f7 (numeric)],
3: [3 - f8 (numeric)],
4: [4 - f9 (numeric)],
5: [5 - f13 (numeric)],
6: [6 - f14 (numeric)],
7: [7 - f15 (numeric)],
8: [8 - f16 (numeric)],
9: [9 - f17 (numeric)],
10: [10 - f18 (numeric)],
11: [11 - f19 (numeric)],
12: [12 - f20 (numer... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 27.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures': 1.0... | pol_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"f5",
"f6",
"f7",
"f8",
"f9",
"f13",
"f14",
"f15",
"f16",
"f17",
"f18",
"f19",
"f20",
"f21",
"f22",
"f23",
"f24",
"f25",
"f26",
"f27",
"f28",
"f29",
"f30",
"f31",
"f32",
"f33"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,041 |
362,622 | predictive_accuracy | accuracy_score | covertype_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset covertype (44121) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - X1 (numeric)],
1: [1 - X2 (numeric)],
2: [2 - X3 (numeric)],
3: [3 - X4 (numeric)],
4: [4 - X5 (numeric)],
5: [5 - X6 (numeric)],
6: [6 - X7 (numeric)],
7: [7 - X8 (numeric)],
8: [8 - X9 (numeric)],
9: [9 - X10 (numeric)],
10: [10 - Y (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | covertype_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"X1",
"X2",
"X3",
"X4",
"X5",
"X6",
"X7",
"X8",
"X9",
"X10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,042 |
362,624 | predictive_accuracy | accuracy_score | covertype_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset covertype (44121) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - X1 (numeric)],
1: [1 - X2 (numeric)],
2: [2 - X3 (numeric)],
3: [3 - X4 (numeric)],
4: [4 - X5 (numeric)],
5: [5 - X6 (numeric)],
6: [6 - X7 (numeric)],
7: [7 - X8 (numeric)],
8: [8 - X9 (numeric)],
9: [9 - X10 (numeric)],
10: [10 - Y (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0... | covertype_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"X1",
"X2",
"X3",
"X4",
"X5",
"X6",
"X7",
"X8",
"X9",
"X10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,043 |
362,647 | predictive_accuracy | accuracy_score | bank-marketing_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset bank-marketing (44126) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_ma... | {0: [0 - V1 (numeric)],
1: [1 - V6 (numeric)],
2: [2 - V10 (numeric)],
3: [3 - V12 (numeric)],
4: [4 - V13 (numeric)],
5: [5 - V14 (numeric)],
6: [6 - V15 (numeric)],
7: [7 - Class (nominal)]} | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
... | bank-marketing_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V6",
"V10",
"V12",
"V13",
"V14",
"V15"
] | [
false,
false,
false,
false,
false,
false,
false
] | 3,044 |
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