uid int64 2 364k | orig_metric stringclasses 30
values | sklearn_metric stringclasses 9
values | dataset_name stringlengths 2 124 | dataset_description stringlengths 3 13k ⌀ | dataset_features stringlengths 41 3.57M | task_description stringlengths 627 762 | task_name stringlengths 2 124 | attribute_names listlengths 0 100k | categorical_indicator listlengths 0 100k | __index_level_0__ int64 0 3.8k |
|---|---|---|---|---|---|---|---|---|---|---|
75,227 | predictive_accuracy | accuracy_score | phoneme | **Author**: Dominique Van Cappel, THOMSON-SINTRA
**Source**: [KEEL](http://sci2s.ugr.es/keel/dataset.php?cod=105#sub2), [ELENA](https://www.elen.ucl.ac.be/neural-nets/Research/Projects/ELENA/databases/REAL/phoneme/) - 1993
**Please cite**: None
The aim of this dataset is to distinguish between nasal (class 0) an... | {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': 3818.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 1586.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 5404.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
... | phoneme | [
"V1",
"V2",
"V3",
"V4",
"V5"
] | [
false,
false,
false,
false,
false
] | 2,509 |
75,222 | predictive_accuracy | accuracy_score | analcatdata_halloffame | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Player (nominal)],
1: [1 - Number_seasons (numeric)],
2: [2 - Games_played (numeric)],
3: [3 - At_bats (numeric)],
4: [4 - Runs (numeric)],
5: [5 - Hits (numeric)],
6: [6 - Doubles (numeric)],
7: [7 - Triples (numeric)],
8: [8 - Home_runs (numeric)],
9: [9 - RBIs (numeric)],
10: [10 - Walks (numeric)... | {'MajorityClassSize': 1215.0,
'MaxNominalAttDistinctValues': 7.0,
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'NumberOfClasses': 2.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 1340.0,
'NumberOfInstancesWithMissingValues': 20.0,
'NumberOfMissingValues': 20.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 2.... | analcatdata_halloffame | [
"Number_seasons",
"Games_played",
"At_bats",
"Runs",
"Hits",
"Doubles",
"Triples",
"Home_runs",
"RBIs",
"Walks",
"Strikeouts",
"Batting_average",
"On_base_pct",
"Slugging_pct",
"Fielding_ave",
"Position"
] | [
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] | 2,510 |
75,153 | predictive_accuracy | accuracy_score | puma32H | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - theta1 (numeric)],
1: [1 - theta2 (numeric)],
2: [2 - theta3 (numeric)],
3: [3 - theta4 (numeric)],
4: [4 - theta5 (numeric)],
5: [5 - theta6 (numeric)],
6: [6 - thetad1 (numeric)],
7: [7 - thetad2 (numeric)],
8: [8 - thetad3 (numeric)],
9: [9 - thetad4 (numeric)],
10: [10 - thetad5 (numeric)],
11: ... | {'MajorityClassSize': 4128.0,
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'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0... | puma32H | [
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75,238 | predictive_accuracy | accuracy_score | car | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {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 - binaryClass (nominal)]} | {'MajorityClassSize': 1210.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 518.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1728.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
... | car | [
"buying",
"maint",
"doors",
"persons",
"lug_boot",
"safety"
] | [
true,
true,
true,
true,
true,
true
] | 2,512 |
4,664 | predictive_accuracy | accuracy_score | AP_Omentum_Prostate | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
1: [1 - 1007_s_at (numeric)],
2: [2 - 121_at (numeric)],
3: [3 - 1405_i_at (numeric)],
4: [4 - 1552256_a_at (numeric)],
5: [5 - 1552257_a_at (numeric)],
6: [6 - 1552289_a_at (numeric)],
7: [7 - 1552309_a_at (numeric)],
8: [8 - 1552348_at (numeric)],
9: [9 - 1552365_at (numeric)],
1... | {'MajorityClassSize': 77.0,
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'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1.... | AP_Omentum_Prostate | [
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"121_at",
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f... | 2,513 |
75,212 | predictive_accuracy | accuracy_score | colleges_usnews | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - FICE (numeric)],
1: [1 - College_name (nominal)],
2: [2 - State (nominal)],
3: [3 - Public/private_indicator (numeric)],
4: [4 - Average_Math_SAT_score (numeric)],
5: [5 - Average_Verbal_SAT_score (numeric)],
6: [6 - Average_Combined_SAT_score (numeric)],
7: [7 - Average_ACT_score (numeric)],
8: [8 - F... | {'MajorityClassSize': 688.0,
'MaxNominalAttDistinctValues': 51.0,
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'NumberOfClasses': 2.0,
'NumberOfFeatures': 34.0,
'NumberOfInstances': 1302.0,
'NumberOfInstancesWithMissingValues': 1144.0,
'NumberOfMissingValues': 7830.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures'... | colleges_usnews | [
"FICE",
"State",
"Public/private_indicator",
"Average_Math_SAT_score",
"Average_Verbal_SAT_score",
"Average_Combined_SAT_score",
"Average_ACT_score",
"First_quartile-Math_SAT",
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] | 2,514 |
4,680 | predictive_accuracy | accuracy_score | AP_Endometrium_Prostate | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
1: [1 - 1007_s_at (numeric)],
2: [2 - 121_at (numeric)],
3: [3 - 1405_i_at (numeric)],
4: [4 - 1438_at (numeric)],
5: [5 - 1552257_a_at (numeric)],
6: [6 - 1552309_a_at (numeric)],
7: [7 - 1552348_at (numeric)],
8: [8 - 1552349_a_at (numeric)],
9: [9 - 1552365_at (numeric)],
10: [1... | {'MajorityClassSize': 69.0,
'MaxNominalAttDistinctValues': 2.0,
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'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1.... | AP_Endometrium_Prostate | [
"1007_s_at",
"121_at",
"1405_i_at",
"1438_at",
"1552257_a_at",
"1552309_a_at",
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"1552615_at",
"1552621_at",
"1552622_s_at",
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"1552... | [
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f... | 2,515 |
75,133 | predictive_accuracy | accuracy_score | mc1 | **Author**: Mike Chapman, NASA
**Source**: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/mc1.html) - 2004
**Please cite**: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, C... | {0: [0 - LOC_BLANK (numeric)],
1: [1 - BRANCH_COUNT (numeric)],
2: [2 - CALL_PAIRS (numeric)],
3: [3 - LOC_CODE_AND_COMMENT (numeric)],
4: [4 - LOC_COMMENTS (numeric)],
5: [5 - CONDITION_COUNT (numeric)],
6: [6 - CYCLOMATIC_COMPLEXITY (numeric)],
7: [7 - CYCLOMATIC_DENSITY (numeric)],
8: [8 - DECISION_COUNT (nu... | {'MajorityClassSize': 9398.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 68.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 9466.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 38.0,
'NumberOfSymbolicFeatures': 1.0,
... | mc1 | [
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"CALL_PAIRS",
"LOC_CODE_AND_COMMENT",
"LOC_COMMENTS",
"CONDITION_COUNT",
"CYCLOMATIC_COMPLEXITY",
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"DECISION_COUNT",
"DESIGN_COMPLEXITY",
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f... | 2,516 |
75,216 | predictive_accuracy | accuracy_score | splice | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Instance_name (nominal)],
1: [1 - attribute_1 (nominal)],
2: [2 - attribute_2 (nominal)],
3: [3 - attribute_3 (nominal)],
4: [4 - attribute_4 (nominal)],
5: [5 - attribute_5 (nominal)],
6: [6 - attribute_6 (nominal)],
7: [7 - attribute_7 (nominal)],
8: [8 - attribute_8 (nominal)],
9: [9 - attribute_9 ... | {'MajorityClassSize': 1655.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 1535.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 3190.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 61.0... | splice | [
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75,217 | predictive_accuracy | accuracy_score | cardiotocography | **Author**: J. P. Marques de Sá, J. Bernardes, D. Ayers de Campos.
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Cardiotocography)
**Please cite**: Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318, [UCI](https://archive.ics.uci.... | {0: [0 - V1 (numeric)],
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3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
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6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 579.0,
'MaxNominalAttDistinctValues': 10.0,
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'NumberOfClasses': 10.0,
'NumberOfFeatures': 36.0,
'NumberOfInstances': 2126.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 35.0,
'NumberOfSymbolicFeatures': 1.0,... | cardiotocography | [
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75,226 | predictive_accuracy | accuracy_score | JapaneseVowels | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - utterance (numeric)],
1: [1 - frame (numeric)],
2: [2 - coefficient1 (numeric)],
3: [3 - coefficient2 (numeric)],
4: [4 - coefficient3 (numeric)],
5: [5 - coefficient4 (numeric)],
6: [6 - coefficient5 (numeric)],
7: [7 - coefficient6 (numeric)],
8: [8 - coefficient7 (numeric)],
9: [9 - coefficient8 (n... | {'MajorityClassSize': 8347.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 1614.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 9961.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 1.0... | JapaneseVowels | [
"utterance",
"frame",
"coefficient1",
"coefficient2",
"coefficient3",
"coefficient4",
"coefficient5",
"coefficient6",
"coefficient7",
"coefficient8",
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] | 2,519 |
145,791 | predictive_accuracy | accuracy_score | appendicitis | **Author**: S. M. Weiss,C. A. Kulikowski
**Source**: KEEL
**Please cite**:
A copy of the data set proposed in: S. M. Weiss, and C. A. Kulikowski, Computer Systems That Learn (1991). | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - Class (nominal)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
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'NumberOfInstancesWithMissingValues': 0.0,
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'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost_... | appendicitis | [
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"V3",
"V4",
"V5",
"V6",
"V7"
] | [
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145,795 | predictive_accuracy | accuracy_score | cloud | **Author**:
**Source**: Unknown -
**Please cite**:
Data from StatLib (ftp stat.cmu.edu/datasets)
These data are those collected in a cloud-seeding experiment in Tasmania
between mid-1964 and January 1971. Their analysis, using regression
techniques and permutation tests, is discussed in:
Miller... | {0: [0 - period (numeric)],
1: [1 - seeded (nominal)],
2: [2 - season (nominal)],
3: [3 - NC (numeric)],
4: [4 - SC (numeric)],
5: [5 - NWC (numeric)],
6: [6 - TE (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
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'NumberOfInstances': 108.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 2.0,
'cost_... | cloud | [
"seeded",
"NC",
"SC",
"NWC",
"TE"
] | [
true,
false,
false,
false,
false
] | 2,521 |
75,155 | predictive_accuracy | accuracy_score | optdigits | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - input1 (numeric)],
1: [1 - input2 (numeric)],
2: [2 - input3 (numeric)],
3: [3 - input4 (numeric)],
4: [4 - input5 (numeric)],
5: [5 - input6 (numeric)],
6: [6 - input7 (numeric)],
7: [7 - input8 (numeric)],
8: [8 - input9 (numeric)],
9: [9 - input10 (numeric)],
10: [10 - input11 (numeric)],
11: [11... | {'MajorityClassSize': 5048.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 572.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 65.0,
'NumberOfInstances': 5620.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 64.0,
'NumberOfSymbolicFeatures': 1.0,... | optdigits | [
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75,242 | predictive_accuracy | accuracy_score | wall-robot-navigation | **Author**: Ananda Freire, Marcus Veloso and Guilherme Barreto
**Source**: [original](http://www.openml.org/d/1497) - UCI
**Please cite**:
* Dataset Title:
Wall-Following Robot Navigation Data Data Set (version with 4 Attributes)
* Abstract:
The data were collected as the SCITOS G5 robot navigates t... | {0: [0 - V1 (numeric)],
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... | wall-robot-navigation | [
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"V2",
"V3",
"V4"
] | [
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] | 2,523 |
75,229 | predictive_accuracy | accuracy_score | waveform-5000 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - x1 (numeric)],
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7: [7 - x8 (numeric)],
8: [8 - x9 (numeric)],
9: [9 - x10 (numeric)],
10: [10 - x11 (numeric)],
11: [11 - x12 (numeric)],
12: [12 - x13 (numeric)]... | {'MajorityClassSize': 3308.0,
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'NumberOfClasses': 2.0,
'NumberOfFeatures': 41.0,
'NumberOfInstances': 5000.0,
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146,156 | precision | precision_score | Dexter | DEXTER is a text classification problem in a bag-of-word representation. This is a two-class classification problem with sparse continuous input variables. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.
Source:
a. Original owners
The original data set we used is a subset of the we... | {0: [0 - V0 (numeric)],
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125,917 | run_cpu_time | accuracy_score | abalone | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title of Database: Abalone data
2. Sources:
(a) Original owners of database:
Marine Resources Division
Marine Research Laboratories - Taroona
Department of Primary Industry and Fisheries, Tasmania
GPO Box 619F, Hobart, Tasmania 7001, Austr... | {0: [0 - Sex (nominal)],
1: [1 - Length (numeric)],
2: [2 - Diameter (numeric)],
3: [3 - Height (numeric)],
4: [4 - Whole_weight (numeric)],
5: [5 - Shucked_weight (numeric)],
6: [6 - Viscera_weight (numeric)],
7: [7 - Shell_weight (numeric)],
8: [8 - Class_number_of_rings (nominal)]} | {'MajorityClassSize': 689.0,
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'NumberOfInstancesWithMissingValues': 0.0,
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'... | abalone | [
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"Length",
"Diameter",
"Height",
"Whole_weight",
"Shucked_weight",
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"Shell_weight"
] | [
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75,234 | predictive_accuracy | accuracy_score | ringnorm | **Author**: Michael Revow
**Source**: http://www.cs.toronto.edu/~delve/data/ringnorm/desc.html
**Please cite**:
1: Abstract:
This is a 20 dimensional, 2 class classification problem. Each class is drawn from a multivariate normal distribution. Class 1 has mean zero and covariance 4 times the identity. C... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
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8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 3736.0,
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'NumberOfInstances': 7400.0,
'NumberOfInstancesWithMissingValues': 0.0,
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'NumberOfSymbolicFeatures': 1.0... | ringnorm | [
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75,221 | predictive_accuracy | accuracy_score | first-order-theorem-proving | **Author**: James P Bridge, Sean B Holden and Lawrence C Paulson
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/First-order+theorem+proving)
**Please cite**: James P Bridge, Sean B Holden and Lawrence C Paulson . Machine learning for first-order theorem proving: learning to select a good heuristic. Jo... | {0: [0 - V1 (numeric)],
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145,954 | precision | precision_score | labor | **Author**: Unknown
**Source**: Collective Barganing Review, Labour Canada
**Please cite**: https://archive.ics.uci.edu/ml/citation_policy.html
Date: Tue, 15 Nov 88 15:44:08 EST
From: stan <stan@csi2.UofO.EDU>
To: aha@ICS.UCI.EDU
1. Title: Final settlements in labor negotitions in Canadian industry
2. Source I... | {0: [0 - duration (numeric)],
1: [1 - wage-increase-first-year (numeric)],
2: [2 - wage-increase-second-year (numeric)],
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4: [4 - cost-of-living-adjustment (nominal)],
5: [5 - working-hours (numeric)],
6: [6 - pension (nominal)],
7: [7 - standby-pay (numeric)],
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75,247 | predictive_accuracy | accuracy_score | page-blocks | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - height (numeric)],
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3: [3 - eccen (numeric)],
4: [4 - p_black (numeric)],
5: [5 - p_and (numeric)],
6: [6 - mean_tr (numeric)],
7: [7 - blackpix (numeric)],
8: [8 - blackand (numeric)],
9: [9 - wb_trans (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 4913.0,
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'MinorityClassSize': 560.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
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'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,... | page-blocks | [
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"area",
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"p_and",
"mean_tr",
"blackpix",
"blackand",
"wb_trans"
] | [
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] | 2,530 |
75,179 | predictive_accuracy | accuracy_score | bank32nh | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - a1cx (numeric)],
1: [1 - a1cy (numeric)],
2: [2 - a1sx (numeric)],
3: [3 - a1sy (numeric)],
4: [4 - a1rho (numeric)],
5: [5 - a1pop (numeric)],
6: [6 - a2cx (numeric)],
7: [7 - a2cy (numeric)],
8: [8 - a2sx (numeric)],
9: [9 - a2sy (numeric)],
10: [10 - a2rho (numeric)],
11: [11 - a2pop (numeric)],
... | {'MajorityClassSize': 5649.0,
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'NumberOfInstancesWithMissingValues': 0.0,
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'NumberOfSymbolicFeatures': 1.0... | bank32nh | [
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75,235 | predictive_accuracy | accuracy_score | wall-robot-navigation | **Author**: Ananda Freire, Marcus Veloso and Guilherme Barreto
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Wall-Following+Robot+Navigation+Data) - 2010
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**Wall-Following Robot Navigation Data Data Set**
The data were coll... | {0: [0 - V1 (numeric)],
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'NumberOfSymbolicFeatures': 1.0,... | wall-robot-navigation | [
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145,958 | precision | precision_score | lymph | **Author**:
**Source**: Unknown -
**Please cite**:
Citation Request:
This lymphography domain was obtained from the University Medical Centre,
Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and
M. Soklic for providing the data. Please include this citation if you plan
... | {0: [0 - lymphatics (nominal)],
1: [1 - block_of_affere (nominal)],
2: [2 - bl_of_lymph_c (nominal)],
3: [3 - bl_of_lymph_s (nominal)],
4: [4 - by_pass (nominal)],
5: [5 - extravasates (nominal)],
6: [6 - regeneration_of (nominal)],
7: [7 - early_uptake_in (nominal)],
8: [8 - lym_nodes_dimin (numeric)],
9: [9 ... | {'MajorityClassSize': 81.0,
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'co... | lymph | [
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75,225 | predictive_accuracy | accuracy_score | ozone-level-8hr | **Author**: Kun Zhang, Wei Fan, XiaoJing Yuan
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/ozone+level+detection)
**Please cite**:
Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond, Knowledge and Information Systems, Vol. 14, No. 3, 2008.
1 . Abstract:
Two ground ozo... | {0: [0 - V1 (numeric)],
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'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 72.0,
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145,792 | predictive_accuracy | accuracy_score | thyroid-allhyper | General Description of Thyroid Disease Databases
and Related Files
This directory contains 6 databases, corresponding test set, and
corresponding documentation. They were left at the University of
California at Irvine by Ross Quinlan during his visit in 1987 for
the 1987 Machine Learning Wor... | {0: [0 - V1 (numeric)],
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'NumberOfSymbolicFeatures': 21.0,
... | thyroid-allhyper | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
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"V12",
"V13",
"V14",
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] | 2,535 |
145,680 | area_under_roc_curve | roc_auc_score | thyroid-allbp | UCI Thyroid allbp dataset. | {0: [0 - V1 (numeric)],
1: [1 - V2 (nominal)],
2: [2 - V3 (nominal)],
3: [3 - V4 (nominal)],
4: [4 - V5 (nominal)],
5: [5 - V6 (nominal)],
6: [6 - V7 (nominal)],
7: [7 - V8 (nominal)],
8: [8 - V9 (nominal)],
9: [9 - V10 (nominal)],
10: [10 - V11 (nominal)],
11: [11 - V12 (nominal)],
12: [12 - V13 (nominal)]... | {'MajorityClassSize': 1632.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 31.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 27.0,
'NumberOfInstances': 2800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 21.0,
... | thyroid-allbp | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26"
] | [
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true,
false,
true,
false,
true,
false,
true,
false,
true,
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] | 2,536 |
145,959 | precision | precision_score | balance-scale | **Author**: Siegler, R. S. (donated by Tim Hume)
**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/balance+scale) - 1994
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**Balance Scale Weight & Distance Database**
This data set was generated to model psychological experiment... | {0: [0 - left-weight (numeric)],
1: [1 - left-distance (numeric)],
2: [2 - right-weight (numeric)],
3: [3 - right-distance (numeric)],
4: [4 - class (nominal)]} | {'MajorityClassSize': 288.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 49.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 625.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | balance-scale | [
"left-weight",
"left-distance",
"right-weight",
"right-distance"
] | [
false,
false,
false,
false
] | 2,537 |
75,232 | predictive_accuracy | accuracy_score | qsar-biodeg | **Author**: Kamel Mansouri, Tine Ringsted, Davide Ballabio
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/QSAR+biodegradation)
**Please cite**: Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R., Consonni, V. (2013). Quantitative Structure - Activity Relationship models for ready biodegradability o... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 699.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 356.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 42.0,
'NumberOfInstances': 1055.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 41.0,
'NumberOfSymbolicFeatures': 1.0,
... | qsar-biodeg | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
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"V16",
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"V36",
"... | [
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f... | 2,538 |
52,945 | predictive_accuracy | accuracy_score | breast-cancer-dropped-missing-attributes-values | **Author**: Smite Chow
**Source**: http://www.openml.org/d/13 - Date 11 July 1988
**Please cite**:
Citation Request: This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. Please inclu... | {0: [0 - age (nominal)],
1: [1 - menopause (nominal)],
2: [2 - tumor-size (nominal)],
3: [3 - inv-nodes (nominal)],
4: [4 - node-caps (nominal)],
5: [5 - deg-malig (nominal)],
6: [6 - breast (nominal)],
7: [7 - breast-quad (nominal)],
8: [8 - irradiat (nominal)],
9: [9 - Class (nominal)]} | {'MajorityClassSize': 196.0,
'MaxNominalAttDistinctValues': 11.0,
'MinorityClassSize': 81.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 277.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 10.0,
... | breast-cancer-dropped-missing-attributes-values | [
"age",
"menopause",
"tumor-size",
"inv-nodes",
"node-caps",
"deg-malig",
"breast",
"breast-quad",
"irradiat"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,539 |
145,681 | area_under_roc_curve | roc_auc_score | wine-quality-white | Citation Request:
This dataset is public available for research. The details are described in [Cortez et al., 2009].
Please include this citation if you plan to use this database:
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
Modeling wine preferences by data mining from physicochemical propertie... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - Class (nominal)]} | {'MajorityClassSize': 2198.0,
'MaxNominalAttDistinctValues': 7.0,
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'NumberOfClasses': 7.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 4898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0,
... | wine-quality-white | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
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] | [
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] | 2,540 |
75,233 | predictive_accuracy | accuracy_score | cpu_act | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - pgout (numeric)],
10: [10 - ppgout (numeric)],
11: [11 - pgfree (n... | {'MajorityClassSize': 5715.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 2477.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0... | cpu_act | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"pgout",
"ppgout",
"pgfree",
"pgscan",
"atch",
"pgin",
"ppgin",
"pflt",
"vflt",
"runqsz",
"freemem",
"freeswap"
] | [
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false,
false,
false
] | 2,541 |
75,249 | predictive_accuracy | accuracy_score | hypothyroid | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - on thyroxine (nominal)],
3: [3 - query on thyroxine (nominal)],
4: [4 - on antithyroid medication (nominal)],
5: [5 - sick (nominal)],
6: [6 - pregnant (nominal)],
7: [7 - thyroid surgery (nominal)],
8: [8 - I131 treatment (nominal)],
9: [9 - query hypot... | {'MajorityClassSize': 3481.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 291.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 3772.0,
'NumberOfInstancesWithMissingValues': 3772.0,
'NumberOfMissingValues': 6064.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures':... | hypothyroid | [
"age",
"sex",
"on thyroxine",
"query on thyroxine",
"on antithyroid medication",
"sick",
"pregnant",
"thyroid surgery",
"I131 treatment",
"query hypothyroid",
"query hyperthyroid",
"lithium",
"goitre",
"tumor",
"hypopituitary",
"psych",
"TSH measured",
"TSH",
"T3 measured",
"T3... | [
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] | 2,542 |
145,957 | precision | precision_score | audiology | **Author**: Professor Jergen at Baylor College of Medicine
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Audiology+(Standardized))
**Please cite**: Bareiss, E. Ray, & Porter, Bruce (1987). Protos: An Exemplar-Based Learning Apprentice. In the Proceedings of the 4th International Workshop on Machine Learning... | {0: [0 - age_gt_60 (nominal)],
1: [1 - air (nominal)],
2: [2 - airBoneGap (nominal)],
3: [3 - ar_c (nominal)],
4: [4 - ar_u (nominal)],
5: [5 - bone (nominal)],
6: [6 - boneAbnormal (nominal)],
7: [7 - bser (nominal)],
8: [8 - history_buzzing (nominal)],
9: [9 - history_dizziness (nominal)],
10: [10 - history... | {'MajorityClassSize': 57.0,
'MaxNominalAttDistinctValues': 24.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 24.0,
'NumberOfFeatures': 70.0,
'NumberOfInstances': 226.0,
'NumberOfInstancesWithMissingValues': 222.0,
'NumberOfMissingValues': 317.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 70.0... | audiology | [
"age_gt_60",
"air",
"airBoneGap",
"ar_c",
"ar_u",
"bone",
"boneAbnormal",
"bser",
"history_buzzing",
"history_dizziness",
"history_fluctuating",
"history_fullness",
"history_heredity",
"history_nausea",
"history_noise",
"history_recruitment",
"history_ringing",
"history_roaring",
... | [
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true,
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true,
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true,
true,
true,
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true... | 2,544 |
145,963 | precision | precision_score | mfeat-morphological | **Author**: Robert P.W. Duin, Department of Applied Physics, Delft University of Technology
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**Multiple Features Dataset: Morphological**
One of a set ... | {0: [0 - att1 (numeric)],
1: [1 - att2 (numeric)],
2: [2 - att3 (numeric)],
3: [3 - att4 (numeric)],
4: [4 - att5 (numeric)],
5: [5 - att6 (numeric)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | mfeat-morphological | [
"att1",
"att2",
"att3",
"att4",
"att5",
"att6"
] | [
false,
false,
false,
false,
false,
false
] | 2,545 |
145,955 | precision | precision_score | arrhythmia | **Author**: H. Altay Guvenir, Burak Acar, Haldun Muderrisoglu
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/arrhythmia)
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**Cardiac Arrhythmia Database**
The aim is to determine the type of arrhythmia from the ECG recordings. ... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - height (numeric)],
3: [3 - weight (numeric)],
4: [4 - QRSduration (numeric)],
5: [5 - PRinterval (numeric)],
6: [6 - Q-Tinterval (numeric)],
7: [7 - Tinterval (numeric)],
8: [8 - Pinterval (numeric)],
9: [9 - QRS (numeric)],
10: [10 - T (numeric)],
11:... | {'MajorityClassSize': 245.0,
'MaxNominalAttDistinctValues': 13.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 13.0,
'NumberOfFeatures': 280.0,
'NumberOfInstances': 452.0,
'NumberOfInstancesWithMissingValues': 384.0,
'NumberOfMissingValues': 408.0,
'NumberOfNumericFeatures': 206.0,
'NumberOfSymbolicFeatures': ... | arrhythmia | [
"age",
"sex",
"height",
"weight",
"QRSduration",
"PRinterval",
"Q-Tinterval",
"Tinterval",
"Pinterval",
"QRS",
"T",
"P",
"QRST",
"J",
"heartrate",
"chDI_Qwave",
"chDI_Rwave",
"chDI_Swave",
"chDI_RPwave",
"chDI_SPwave",
"chDI_intrinsicReflecttions",
"chDI_RRwaveExists",
"c... | [
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tr... | 2,546 |
75,211 | predictive_accuracy | accuracy_score | autoUniv-au4-2500 | **Author**: Ray. J. Hickey
**Source**: UCI
**Please cite**:
* Dataset Title:
AutoUniv Dataset
data problem: autoUniv-au4-2500
* Abstract:
AutoUniv is an advanced data generator for classifications tasks. The aim is to reflect the nuances and heterogeneity of real data. Data can be generated in .c... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (nominal)],
3: [3 - V4 (nominal)],
4: [4 - V5 (numeric)],
5: [5 - V6 (nominal)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (nominal)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (nominal)],
12: [12 - V13 (nominal)]... | {'MajorityClassSize': 1173.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 196.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 58.0,
'NumberOfSymbolicFeatures': 43.... | autoUniv-au4-2500 | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
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"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
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145,977 | precision | precision_score | ecoli | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title: Protein Localization Sites
2. Creator and Maintainer:
Kenta Nakai
Institue of Molecular and Cellular Biology
Osaka, University
1-3 Yamada-oka, Suita 565 Japan
nakai@imcb.osaka-u.ac.jp
http... | {0: [0 - mcg (numeric)],
1: [1 - gvh (numeric)],
2: [2 - lip (numeric)],
3: [3 - chg (numeric)],
4: [4 - aac (numeric)],
5: [5 - alm1 (numeric)],
6: [6 - alm2 (numeric)],
7: [7 - class (nominal)]} | {'MajorityClassSize': 143.0,
'MaxNominalAttDistinctValues': 8.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 336.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | ecoli | [
"mcg",
"gvh",
"lip",
"chg",
"aac",
"alm1",
"alm2"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,549 |
145,953 | precision | precision_score | kr-vs-kp | Author: Alen Shapiro
Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Chess+(King-Rook+vs.+King-Pawn))
Please cite: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html)
1. Title: Chess End-Game -- King+Rook versus King+Pawn on a7
(usually abbreviated KRKPA7). The pawn on a7 means it is one s... | {0: [0 - bkblk (nominal)],
1: [1 - bknwy (nominal)],
2: [2 - bkon8 (nominal)],
3: [3 - bkona (nominal)],
4: [4 - bkspr (nominal)],
5: [5 - bkxbq (nominal)],
6: [6 - bkxcr (nominal)],
7: [7 - bkxwp (nominal)],
8: [8 - blxwp (nominal)],
9: [9 - bxqsq (nominal)],
10: [10 - cntxt (nominal)],
11: [11 - dsopp (nom... | {'MajorityClassSize': 1669.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 1527.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 3196.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 37.0... | kr-vs-kp | [
"bkblk",
"bknwy",
"bkon8",
"bkona",
"bkspr",
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"bkxcr",
"bkxwp",
"blxwp",
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"hdchk",
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"qxmsq",
"r2ar8",
"reskd",
"reskr",
"rimmx",
"rkxwp",
"rxmsq",
"simpl",
"skach",
"skewr",
"skrxp",
"spcop",
"stlmt",... | [
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] | 2,550 |
145,952 | precision | precision_score | anneal | **Author**: Unknown. Donated by David Sterling and Wray Buntine
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Annealing) - 1990
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
The original Annealing dataset from UCI. The exact meaning of the features and classes is lar... | {0: [0 - family (nominal)],
1: [1 - product-type (nominal)],
2: [2 - steel (nominal)],
3: [3 - carbon (numeric)],
4: [4 - hardness (numeric)],
5: [5 - temper_rolling (nominal)],
6: [6 - condition (nominal)],
7: [7 - formability (nominal)],
8: [8 - strength (numeric)],
9: [9 - non-ageing (nominal)],
10: [10 - ... | {'MajorityClassSize': 684.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 8.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 898.0,
'NumberOfMissingValues': 22175.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.... | anneal | [
"family",
"product-type",
"steel",
"carbon",
"hardness",
"temper_rolling",
"condition",
"formability",
"strength",
"non-ageing",
"surface-finish",
"surface-quality",
"enamelability",
"bc",
"bf",
"bt",
"bw%2Fme",
"bl",
"m",
"chrom",
"phos",
"cbond",
"marvi",
"exptl",
"... | [
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] | 2,551 |
145,971 | precision | precision_score | page-blocks | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title of Database: Blocks Classification
2. Sources:
(a) Donato Malerba
Dipartimento di Informatica
University of Bari
via Orabona 4
70126 Bari - Italy
phone: +39 - 80 - 5443269
fax: +39 - 80 - 5443196
... | {0: [0 - height (numeric)],
1: [1 - lenght (numeric)],
2: [2 - area (numeric)],
3: [3 - eccen (numeric)],
4: [4 - p_black (numeric)],
5: [5 - p_and (numeric)],
6: [6 - mean_tr (numeric)],
7: [7 - blackpix (numeric)],
8: [8 - blackand (numeric)],
9: [9 - wb_trans (numeric)],
10: [10 - class (nominal)]} | {'MajorityClassSize': 4913.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 28.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 5473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | page-blocks | [
"height",
"lenght",
"area",
"eccen",
"p_black",
"p_and",
"mean_tr",
"blackpix",
"blackand",
"wb_trans"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
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] | 2,552 |
145,974 | precision | precision_score | dermatology | 1. Title: Dermatology Database
2. Source Information:
(a) Original owners:
-- 1. Nilsel Ilter, M.D., Ph.D.,
Gazi University,
School of Medicine
06510 Ankara, Turkey
Phone: +90 (312) 214 1080
-- 2. H. Altay Guvenir, PhD.,
Bilkent Univ... | {0: [0 - erythema (nominal)],
1: [1 - scaling (nominal)],
2: [2 - definite_borders (nominal)],
3: [3 - itching (nominal)],
4: [4 - koebner_phenomenon (nominal)],
5: [5 - polygonal_papules (nominal)],
6: [6 - follicular_papules (nominal)],
7: [7 - oral_mucosal_involvement (nominal)],
8: [8 - knee_and_elbow_invol... | {'MajorityClassSize': 112.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 35.0,
'NumberOfInstances': 366.0,
'NumberOfInstancesWithMissingValues': 8.0,
'NumberOfMissingValues': 8.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 34.0,
'... | dermatology | [
"erythema",
"scaling",
"definite_borders",
"itching",
"koebner_phenomenon",
"polygonal_papules",
"follicular_papules",
"oral_mucosal_involvement",
"knee_and_elbow_involvement",
"scalp_involvement",
"family_history",
"melanin_incontinence",
"eosinophils_in_the_infiltrate",
"PNL_infiltrate",... | [
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] | 2,553 |
145,976 | precision | precision_score | diabetes | **Author**: [Vincent Sigillito](vgs@aplcen.apl.jhu.edu)
**Source**: [Obtained from UCI](https://archive.ics.uci.edu/ml/datasets/pima+indians+diabetes)
**Please cite**: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html)
1. Title: Pima Indians Diabetes Database
2. Sources:
(a) Origi... | {0: [0 - preg (numeric)],
1: [1 - plas (numeric)],
2: [2 - pres (numeric)],
3: [3 - skin (numeric)],
4: [4 - insu (numeric)],
5: [5 - mass (numeric)],
6: [6 - pedi (numeric)],
7: [7 - age (numeric)],
8: [8 - class (nominal)]} | {'MajorityClassSize': 500.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 268.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 768.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | diabetes | [
"preg",
"plas",
"pres",
"skin",
"insu",
"mass",
"pedi",
"age"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 2,554 |
145,970 | precision | precision_score | credit-approval | **Author**: Confidential - Donated by Ross Quinlan
**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/credit+approval) - 1987
**Please cite**: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html)
**Credit Approval**
This file concerns credit card applications. All attribute names and values have been... | {0: [0 - A1 (nominal)],
1: [1 - A2 (numeric)],
2: [2 - A3 (numeric)],
3: [3 - A4 (nominal)],
4: [4 - A5 (nominal)],
5: [5 - A6 (nominal)],
6: [6 - A7 (nominal)],
7: [7 - A8 (numeric)],
8: [8 - A9 (nominal)],
9: [9 - A10 (nominal)],
10: [10 - A11 (numeric)],
11: [11 - A12 (nominal)],
12: [12 - A13 (nominal)]... | {'MajorityClassSize': 383.0,
'MaxNominalAttDistinctValues': 14.0,
'MinorityClassSize': 307.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 690.0,
'NumberOfInstancesWithMissingValues': 37.0,
'NumberOfMissingValues': 67.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 10.0... | credit-approval | [
"A1",
"A2",
"A3",
"A4",
"A5",
"A6",
"A7",
"A8",
"A9",
"A10",
"A11",
"A12",
"A13",
"A14",
"A15"
] | [
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true,
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true,
true,
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] | 2,555 |
4,663 | predictive_accuracy | accuracy_score | AP_Omentum_Uterus | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
1: [1 - 1007_s_at (numeric)],
2: [2 - 121_at (numeric)],
3: [3 - 1405_i_at (numeric)],
4: [4 - 1438_at (numeric)],
5: [5 - 1552256_a_at (numeric)],
6: [6 - 1552257_a_at (numeric)],
7: [7 - 1552309_a_at (numeric)],
8: [8 - 1552348_at (numeric)],
9: [9 - 1552368_at (numeric)],
10: [1... | {'MajorityClassSize': 124.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 77.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 201.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Omentum_Uterus | [
"1007_s_at",
"121_at",
"1405_i_at",
"1438_at",
"1552256_a_at",
"1552257_a_at",
"1552309_a_at",
"1552348_at",
"1552368_at",
"1552378_s_at",
"1552426_a_at",
"1552455_at",
"1552456_a_at",
"1552477_a_at",
"1552610_a_at",
"1552619_a_at",
"1552621_at",
"1552622_s_at",
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... | [
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145,972 | precision | precision_score | credit-g | **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': 10.0,
'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 | [
"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... | [
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] | 2,558 |
75,246 | predictive_accuracy | accuracy_score | mfeat-karhunen | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - att1 (numeric)],
1: [1 - att2 (numeric)],
2: [2 - att3 (numeric)],
3: [3 - att4 (numeric)],
4: [4 - att5 (numeric)],
5: [5 - att6 (numeric)],
6: [6 - att7 (numeric)],
7: [7 - att8 (numeric)],
8: [8 - att9 (numeric)],
9: [9 - att10 (numeric)],
10: [10 - att11 (numeric)],
11: [11 - att12 (numeric)],
... | {'MajorityClassSize': 1800.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 200.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 65.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 64.0,
'NumberOfSymbolicFeatures': 1.0,... | mfeat-karhunen | [
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"att2",
"att3",
"att4",
"att5",
"att6",
"att7",
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"att28",
"att29",
"att30... | [
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75,241 | predictive_accuracy | accuracy_score | wall-robot-navigation | **Author**: Ananda Freire, Marcus Veloso and Guilherme Barreto
**Source**: [original](http://www.openml.org/d/1497) - UCI
**Please cite**:
* Dataset Title:
Wall-Following Robot Navigation Data Data Set (version with 2 Attributes)
* Abstract:
The data were collected as the SCITOS G5 robot navigates thro... | {0: [0 - V1 (numeric)], 1: [1 - V2 (numeric)], 2: [2 - Class (nominal)]} | {'MajorityClassSize': 2205.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 328.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 5456.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
... | wall-robot-navigation | [
"V1",
"V2"
] | [
false,
false
] | 2,560 |
75,251 | predictive_accuracy | accuracy_score | mfeat-zernike | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - att1 (numeric)],
1: [1 - att2 (numeric)],
2: [2 - att3 (numeric)],
3: [3 - att4 (numeric)],
4: [4 - att5 (numeric)],
5: [5 - att6 (numeric)],
6: [6 - att7 (numeric)],
7: [7 - att8 (numeric)],
8: [8 - att9 (numeric)],
9: [9 - att10 (numeric)],
10: [10 - att11 (numeric)],
11: [11 - att12 (numeric)],
... | {'MajorityClassSize': 1800.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 200.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 48.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 47.0,
'NumberOfSymbolicFeatures': 1.0,... | mfeat-zernike | [
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f... | 2,561 |
4,694 | predictive_accuracy | accuracy_score | AP_Prostate_Lung | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
1: [1 - 1007_s_at (numeric)],
2: [2 - 117_at (numeric)],
3: [3 - 121_at (numeric)],
4: [4 - 1405_i_at (numeric)],
5: [5 - 1552257_a_at (numeric)],
6: [6 - 1552283_s_at (numeric)],
7: [7 - 1552309_a_at (numeric)],
8: [8 - 1552348_at (numeric)],
9: [9 - 1552365_at (numeric)],
10: [10... | {'MajorityClassSize': 126.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 69.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 195.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Prostate_Lung | [
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"117_at",
"121_at",
"1405_i_at",
"1552257_a_at",
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"1552309_a_at",
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"1552455_at",
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"1552477_a_at",
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"1552619_a_at",
"1552621_at",
"1552622_s_at",
"15526... | [
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f... | 2,562 |
146,024 | precision | precision_score | lung-cancer | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title: Lung Cancer Data
2. Source Information:
- Data was published in :
Hong, Z.Q. and Yang, J.Y. "Optimal Discriminant Plane for a Small
Number of Samples and Design Method of Classifier on the Plane",
Pattern Recognition, Vol. 24, No. ... | {0: [0 - class (nominal)],
1: [1 - attribute2 (nominal)],
2: [2 - attribute3 (nominal)],
3: [3 - attribute4 (nominal)],
4: [4 - attribute5 (nominal)],
5: [5 - attribute6 (nominal)],
6: [6 - attribute7 (nominal)],
7: [7 - attribute8 (nominal)],
8: [8 - attribute9 (nominal)],
9: [9 - attribute10 (nominal)],
10:... | {'MajorityClassSize': 13.0,
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'NumberOfClasses': 3.0,
'NumberOfFeatures': 57.0,
'NumberOfInstances': 32.0,
'NumberOfInstancesWithMissingValues': 5.0,
'NumberOfMissingValues': 5.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 57.0,
'cos... | lung-cancer | [
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75,230 | predictive_accuracy | accuracy_score | one-hundred-plants-shape | **Author**: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set) - 2010
**Please cite**: Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture a... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 16.0,
'MaxNominalAttDistinctValues': 100.0,
'MinorityClassSize': 16.0,
'NumberOfClasses': 100.0,
'NumberOfFeatures': 65.0,
'NumberOfInstances': 1600.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 64.0,
'NumberOfSymbolicFeatures': 1.0... | one-hundred-plants-shape | [
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f... | 2,564 |
145,986 | precision | precision_score | iris | **Author**: R.A. Fisher
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Iris) - 1936 - Donated by Michael Marshall
**Please cite**:
**Iris Plants Database**
This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is ref... | {0: [0 - sepallength (numeric)],
1: [1 - sepalwidth (numeric)],
2: [2 - petallength (numeric)],
3: [3 - petalwidth (numeric)],
4: [4 - class (nominal)]} | {'MajorityClassSize': 50.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 50.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 150.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | iris | [
"sepallength",
"sepalwidth",
"petallength",
"petalwidth"
] | [
false,
false,
false,
false
] | 2,565 |
145,983 | precision | precision_score | heart-statlog | **Author**:
**Source**: Unknown -
**Please cite**:
This database contains 13 attributes (which have been extracted from
a larger set of 75)
Attribute Information:
------------------------
-- 1. age
-- 2. sex
-- 3. chest pain type (4 values)
-... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - chest (numeric)],
3: [3 - resting_blood_pressure (numeric)],
4: [4 - serum_cholestoral (numeric)],
5: [5 - fasting_blood_sugar (numeric)],
6: [6 - resting_electrocardiographic_results (numeric)],
7: [7 - maximum_heart_rate_achieved (numeric)],
8: [8 - exe... | {'MajorityClassSize': 150.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 120.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 270.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 1.0,
... | heart-statlog | [
"age",
"sex",
"chest",
"resting_blood_pressure",
"serum_cholestoral",
"fasting_blood_sugar",
"resting_electrocardiographic_results",
"maximum_heart_rate_achieved",
"exercise_induced_angina",
"oldpeak",
"slope",
"number_of_major_vessels",
"thal"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,566 |
145,975 | precision | precision_score | segment | **Author**: University of Massachusetts Vision Group, Carla Brodley
**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/image+segmentation) - 1990
**Please cite**: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html)
**Image Segmentation Data Set**
The instances were drawn randomly from a database of 7... | {0: [0 - region-centroid-col (numeric)],
1: [1 - region-centroid-row (numeric)],
2: [2 - region-pixel-count (numeric)],
3: [3 - short-line-density-5 (numeric)],
4: [4 - short-line-density-2 (numeric)],
5: [5 - vedge-mean (numeric)],
6: [6 - vegde-sd (numeric)],
7: [7 - hedge-mean (numeric)],
8: [8 - hedge-sd (n... | {'MajorityClassSize': 330.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 330.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 2310.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 1.0,
... | segment | [
"region-centroid-col",
"region-centroid-row",
"region-pixel-count",
"short-line-density-5",
"short-line-density-2",
"vedge-mean",
"vegde-sd",
"hedge-mean",
"hedge-sd",
"intensity-mean",
"rawred-mean",
"rawblue-mean",
"rawgreen-mean",
"exred-mean",
"exblue-mean",
"exgreen-mean",
"valu... | [
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false,
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] | 2,567 |
145,984 | precision | precision_score | ionosphere | **Author**: Space Physics Group, Applied Physics Laboratory, Johns Hopkins University. Donated by Vince Sigillito.
**Source**: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/ionosphere)
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**Johns Hopkins Universit... | {0: [0 - a01 (numeric)],
1: [1 - a02 (numeric)],
2: [2 - a03 (numeric)],
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6: [6 - a07 (numeric)],
7: [7 - a08 (numeric)],
8: [8 - a09 (numeric)],
9: [9 - a10 (numeric)],
10: [10 - a11 (numeric)],
11: [11 - a12 (numeric)],
12: [12 - a13 (... | {'MajorityClassSize': 225.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 126.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 35.0,
'NumberOfInstances': 351.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 34.0,
'NumberOfSymbolicFeatures': 1.0,
... | ionosphere | [
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"a05",
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146,032 | precision | precision_score | primary-tumor | **Author**:
**Source**: Unknown -
**Please cite**:
Citation Request:
This primary tumor domain was obtained from the University Medical Centre,
Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and
M. Soklic for providing the data. Please include this citation if you plan
... | {0: [0 - age (nominal)],
1: [1 - sex (nominal)],
2: [2 - histologic-type (nominal)],
3: [3 - degree-of-diffe (nominal)],
4: [4 - bone (nominal)],
5: [5 - bone-marrow (nominal)],
6: [6 - lung (nominal)],
7: [7 - pleura (nominal)],
8: [8 - peritoneum (nominal)],
9: [9 - liver (nominal)],
10: [10 - brain (nomina... | {'MajorityClassSize': 84.0,
'MaxNominalAttDistinctValues': 21.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 21.0,
'NumberOfFeatures': 18.0,
'NumberOfInstances': 339.0,
'NumberOfInstancesWithMissingValues': 207.0,
'NumberOfMissingValues': 225.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 18.0... | primary-tumor | [
"age",
"sex",
"histologic-type",
"degree-of-diffe",
"bone",
"bone-marrow",
"lung",
"pleura",
"peritoneum",
"liver",
"brain",
"skin",
"neck",
"supraclavicular",
"axillar",
"mediastinum",
"abdominal"
] | [
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true,
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true,
true,
true,
true,
true,
true
] | 2,569 |
75,231 | predictive_accuracy | accuracy_score | one-hundred-plants-texture | **Author**: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set) - 2010
**Please cite**: Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture a... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 16.0,
'MaxNominalAttDistinctValues': 100.0,
'MinorityClassSize': 15.0,
'NumberOfClasses': 100.0,
'NumberOfFeatures': 65.0,
'NumberOfInstances': 1599.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 64.0,
'NumberOfSymbolicFeatures': 1.0... | one-hundred-plants-texture | [
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"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
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"V9",
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"V27",
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"V29",
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"V31",
"V32",
"V33",
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"V35",
"V36",
"... | [
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f... | 2,570 |
146,025 | precision | precision_score | molecular-biology_promoters | **Author**: C. Harley, R. Reynolds, M. Noordewier, J. Shavlik.
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Molecular+Biology+(Promoter+Gene+Sequences)) - 1990
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**E. coli promoter gene sequences (DNA)**
Compilation of promo... | {0: [0 - class (nominal)],
1: [1 - instance (nominal)],
2: [2 - p-50 (nominal)],
3: [3 - p-49 (nominal)],
4: [4 - p-48 (nominal)],
5: [5 - p-47 (nominal)],
6: [6 - p-46 (nominal)],
7: [7 - p-45 (nominal)],
8: [8 - p-44 (nominal)],
9: [9 - p-43 (nominal)],
10: [10 - p-42 (nominal)],
11: [11 - p-41 (nominal)],... | {'MajorityClassSize': 53.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 53.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 58.0,
'NumberOfInstances': 106.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 58.0,
'c... | molecular-biology_promoters | [
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"p-23",
"p-22",
"p-21",
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"p-19"... | [
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146,038 | precision | precision_score | wine | **Author**:
**Source**: Unknown -
**Please cite**:
1. Title of Database: Wine recognition data
Updated Sept 21, 1998 by C.Blake : Added attribute information
2. Sources:
(a) Forina, M. et al, PARVUS - An Extendible Package for Data
Exploration, Classification and Correlation. Institute of Pha... | {0: [0 - class (nominal)],
1: [1 - Alcohol (numeric)],
2: [2 - Malic_acid (numeric)],
3: [3 - Ash (numeric)],
4: [4 - Alcalinity_of_ash (numeric)],
5: [5 - Magnesium (numeric)],
6: [6 - Total_phenols (numeric)],
7: [7 - Flavanoids (numeric)],
8: [8 - Nonflavanoid_phenols (numeric)],
9: [9 - Proanthocyanins (nu... | {'MajorityClassSize': 71.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 48.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 178.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | wine | [
"Alcohol",
"Malic_acid",
"Ash",
"Alcalinity_of_ash",
"Magnesium",
"Total_phenols",
"Flavanoids",
"Nonflavanoid_phenols",
"Proanthocyanins",
"Color_intensity",
"Hue",
"OD280%2FOD315_of_diluted_wines",
"Proline"
] | [
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false,
false,
false,
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false,
false,
false,
false,
false,
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] | 2,572 |
146,055 | precision | precision_score | meta_all.arff | null | {0: [0 - openml_task_id (numeric)],
1: [1 - meta_REPTreeDepth2ErrRate (numeric)],
2: [2 - meta_J48.00001.ErrRate (numeric)],
3: [3 - meta_NBErrRate (numeric)],
4: [4 - meta_MeanMutualInformation (numeric)],
5: [5 - meta_NBAUC (numeric)],
6: [6 - meta_DecisionStumpKappa (numeric)],
7: [7 - meta_HoeffdingDDM.warni... | {'MajorityClassSize': 42.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 63.0,
'NumberOfInstances': 71.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 62.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | meta_all.arff | [
"openml_task_id",
"meta_REPTreeDepth2ErrRate",
"meta_J48.00001.ErrRate",
"meta_NBErrRate",
"meta_MeanMutualInformation",
"meta_NBAUC",
"meta_DecisionStumpKappa",
"meta_HoeffdingDDM.warnings",
"meta_NoiseToSignalRatio",
"meta_RandomTreeDepth3AUC_K=0",
"meta_PercentageOfNumericAtts",
"meta_Equiv... | [
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f... | 2,574 |
145,966 | precision | precision_score | mfeat-zernike | **Author**: Robert P.W. Duin, Department of Applied Physics, Delft University of Technology
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**Multiple Features Dataset: Zernike**
One of a set of 6 d... | {0: [0 - att1 (numeric)],
1: [1 - att2 (numeric)],
2: [2 - att3 (numeric)],
3: [3 - att4 (numeric)],
4: [4 - att5 (numeric)],
5: [5 - att6 (numeric)],
6: [6 - att7 (numeric)],
7: [7 - att8 (numeric)],
8: [8 - att9 (numeric)],
9: [9 - att10 (numeric)],
10: [10 - att11 (numeric)],
11: [11 - att12 (numeric)],
... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 48.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 47.0,
'NumberOfSymbolicFeatures': 1.0... | mfeat-zernike | [
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f... | 2,575 |
145,682 | area_under_roc_curve | roc_auc_score | spectrometer | **Author**:
**Source**: Unknown - 1988
**Please cite**:
1. Title: Part of the IRAS Low Resolution Spectrometer Database
2. Sources:
(a) Originator: Infra-Red Astronomy Satellite Project Database
(b) Donor: John Stutz <STUTZ@pluto.arc.nasa.gov>
(c) Date: March 1988 (approximately)
3. Past Usage: unknown
... | {0: [0 - LRS-name (nominal)],
1: [1 - LRS-class (nominal)],
2: [2 - ID-type (nominal)],
3: [3 - Right-Ascension (numeric)],
4: [4 - Declination (numeric)],
5: [5 - Scale_Factor (numeric)],
6: [6 - Blue_base_1 (numeric)],
7: [7 - Blue_base_2 (numeric)],
8: [8 - Red_base_1 (numeric)],
9: [9 - Red_base_2 (numeric... | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 531.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 48.0,
'NumberOfFeatures': 102.0,
'NumberOfInstances': 531.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 2.0,... | spectrometer | [
"ID-type",
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"Declination",
"Scale_Factor",
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"blue-band-flux_6",
"blue-band-flux_7",
"blue-band-flux_8",
"blue... | [
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fa... | 2,576 |
146,056 | precision | precision_score | meta_batchincremental.arff | null | {0: [0 - openml_task_id (numeric)],
1: [1 - meta_REPTreeDepth2ErrRate (numeric)],
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5: [5 - meta_NBAUC (numeric)],
6: [6 - meta_DecisionStumpKappa (numeric)],
7: [7 - meta_HoeffdingDDM.warni... | {'MajorityClassSize': 50.0,
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'NumberOfSymbolicFeatures': 1.0,
'cos... | meta_batchincremental.arff | [
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f... | 2,577 |
146,057 | precision | precision_score | meta_ensembles.arff | null | {0: [0 - openml_task_id (numeric)],
1: [1 - meta_REPTreeDepth2ErrRate (numeric)],
2: [2 - meta_J48.00001.ErrRate (numeric)],
3: [3 - meta_NBErrRate (numeric)],
4: [4 - meta_MeanMutualInformation (numeric)],
5: [5 - meta_NBAUC (numeric)],
6: [6 - meta_DecisionStumpKappa (numeric)],
7: [7 - meta_HoeffdingDDM.warni... | {'MajorityClassSize': 45.0,
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'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 62.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | meta_ensembles.arff | [
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f... | 2,578 |
146,066 | precision | precision_score | monks-problems-3 | **Author**: Sebastian Thrun (Carnegie Mellon University)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/MONK's+Problems) - October 1992
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**The Monk's Problems: Problem 3**
Once upon a time, in July 1991, the monks of Corsend... | {0: [0 - class (nominal)],
1: [1 - attr1 (nominal)],
2: [2 - attr2 (nominal)],
3: [3 - attr3 (nominal)],
4: [4 - attr4 (nominal)],
5: [5 - attr5 (nominal)],
6: [6 - attr6 (nominal)]} | {'MajorityClassSize': 288.0,
'MaxNominalAttDistinctValues': 4.0,
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'NumberOfClasses': 2.0,
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'NumberOfInstances': 554.0,
'NumberOfInstancesWithMissingValues': 0.0,
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'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | monks-problems-3 | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6"
] | [
true,
true,
true,
true,
true,
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] | 2,579 |
146,064 | precision | precision_score | monks-problems-1 | **Author**: Sebastian Thrun (Carnegie Mellon University)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/MONK's+Problems) - October 1992
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**The Monk's Problems: Problem 1**
Once upon a time, in July 1991, the monks of Corsend... | {0: [0 - class (nominal)],
1: [1 - attr1 (nominal)],
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3: [3 - attr3 (nominal)],
4: [4 - attr4 (nominal)],
5: [5 - attr5 (nominal)],
6: [6 - attr6 (nominal)]} | {'MajorityClassSize': 278.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 278.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 556.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | monks-problems-1 | [
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"attr2",
"attr3",
"attr4",
"attr5",
"attr6"
] | [
true,
true,
true,
true,
true,
true
] | 2,580 |
4,671 | predictive_accuracy | accuracy_score | AP_Omentum_Lung | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
1: [1 - 1007_s_at (numeric)],
2: [2 - 121_at (numeric)],
3: [3 - 1405_i_at (numeric)],
4: [4 - 1552256_a_at (numeric)],
5: [5 - 1552257_a_at (numeric)],
6: [6 - 1552283_s_at (numeric)],
7: [7 - 1552348_at (numeric)],
8: [8 - 1552365_at (numeric)],
9: [9 - 1552368_at (numeric)],
10:... | {'MajorityClassSize': 126.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 77.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 203.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Omentum_Lung | [
"1007_s_at",
"121_at",
"1405_i_at",
"1552256_a_at",
"1552257_a_at",
"1552283_s_at",
"1552348_at",
"1552365_at",
"1552368_at",
"1552426_a_at",
"1552456_a_at",
"1552477_a_at",
"1552610_a_at",
"1552615_at",
"1552619_a_at",
"1552621_at",
"1552622_s_at",
"1552626_a_at",
"1552628_a_at"... | [
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f... | 2,581 |
146,058 | precision | precision_score | meta_instanceincremental.arff | null | {0: [0 - openml_task_id (numeric)],
1: [1 - meta_REPTreeDepth2ErrRate (numeric)],
2: [2 - meta_J48.00001.ErrRate (numeric)],
3: [3 - meta_NBErrRate (numeric)],
4: [4 - meta_MeanMutualInformation (numeric)],
5: [5 - meta_NBAUC (numeric)],
6: [6 - meta_DecisionStumpKappa (numeric)],
7: [7 - meta_HoeffdingDDM.warni... | {'MajorityClassSize': 54.0,
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'NumberOfInstancesWithMissingValues': 0.0,
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'NumberOfNumericFeatures': 62.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | meta_instanceincremental.arff | [
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"meta_MeanMutualInformation",
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"meta_DecisionStumpKappa",
"meta_HoeffdingDDM.warnings",
"meta_NoiseToSignalRatio",
"meta_RandomTreeDepth3AUC_K=0",
"meta_PercentageOfNumericAtts",
"meta_Equiv... | [
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f... | 2,582 |
146,063 | precision | precision_score | hayes-roth | **Author**: Barbara and Frederick Hayes-Roth
**Source**: [original](https://archive.ics.uci.edu/ml/datasets/Hayes-Roth) -
**Please cite**:
Hayes-Roth Database
This is a merged version of the separate train and test set which are usually distributed. On OpenML this train-test split can be found as one of th... | {0: [0 - hobby (numeric)],
1: [1 - age (numeric)],
2: [2 - educational_level (numeric)],
3: [3 - marital_status (numeric)],
4: [4 - class (nominal)]} | {'MajorityClassSize': 65.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 31.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 160.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | hayes-roth | [
"hobby",
"age",
"educational_level",
"marital_status"
] | [
false,
false,
false,
false
] | 2,583 |
145,962 | precision | precision_score | mfeat-karhunen | **Author**: Robert P.W. Duin, Department of Applied Physics, Delft University of Technology
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**Multiple Features Dataset: Karhunen**
One of a set of 6 ... | {0: [0 - att1 (numeric)],
1: [1 - att2 (numeric)],
2: [2 - att3 (numeric)],
3: [3 - att4 (numeric)],
4: [4 - att5 (numeric)],
5: [5 - att6 (numeric)],
6: [6 - att7 (numeric)],
7: [7 - att8 (numeric)],
8: [8 - att9 (numeric)],
9: [9 - att10 (numeric)],
10: [10 - att11 (numeric)],
11: [11 - att12 (numeric)],
... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 65.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 64.0,
'NumberOfSymbolicFeatures': 1.0... | mfeat-karhunen | [
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"att2",
"att3",
"att4",
"att5",
"att6",
"att7",
"att8",
"att9",
"att10",
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"att12",
"att13",
"att14",
"att15",
"att16",
"att17",
"att18",
"att19",
"att20",
"att21",
"att22",
"att23",
"att24",
"att25",
"att26",
"att27",
"att28",
"att29",
"att30... | [
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f... | 2,584 |
146,065 | precision | precision_score | monks-problems-2 | **Author**: Sebastian Thrun (Carnegie Mellon University)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/MONK's+Problems) - October 1992
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**The Monk's Problems: Problem 2**
Once upon a time, in July 1991, the monks of Corsend... | {0: [0 - class (nominal)],
1: [1 - attr1 (nominal)],
2: [2 - attr2 (nominal)],
3: [3 - attr3 (nominal)],
4: [4 - attr4 (nominal)],
5: [5 - attr5 (nominal)],
6: [6 - attr6 (nominal)]} | {'MajorityClassSize': 395.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 206.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 601.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | monks-problems-2 | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6"
] | [
true,
true,
true,
true,
true,
true
] | 2,585 |
146,068 | precision | precision_score | SyskillWebert-Sheep | **Author**: Michael Pazzani (pazzani@ics.uci.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Syskill+and+Webert+Web+Page+Ratings)- 1999
**Please cite**:
**Syskill and Webert Web Page Ratings**
This database contains the HTML source of web pages plus the ratings of a single user on these web pag... | {0: [0 - id (string)], 1: [1 - text (string)], 2: [2 - class (nominal)]} | {'MajorityClassSize': 51.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 14.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 65.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | SyskillWebert-Sheep | [
"id",
"text"
] | [
false,
false
] | 2,586 |
146,076 | precision | precision_score | molecular-biology_promoters | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - class (nominal)],
1: [1 - instance (nominal)],
2: [2 - p-50 (nominal)],
3: [3 - p-49 (nominal)],
4: [4 - p-48 (nominal)],
5: [5 - p-47 (nominal)],
6: [6 - p-46 (nominal)],
7: [7 - p-45 (nominal)],
8: [8 - p-44 (nominal)],
9: [9 - p-43 (nominal)],
10: [10 - p-42 (nominal)],
11: [11 - p-41 (nominal)],... | {'MajorityClassSize': 72.0,
'MaxNominalAttDistinctValues': 4.0,
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'NumberOfClasses': 2.0,
'NumberOfFeatures': 58.0,
'NumberOfInstances': 106.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 58.0,
'c... | molecular-biology_promoters | [
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"p-31",
"p-30",
"p-29",
"p-28",
"p-27",
"p-26",
"p-25",
"p-24",
"p-23",
"p-22",
"p-21",
"p-20",
"p-19"... | [
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146,072 | precision | precision_score | schizo | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Schizophrenic Eye-Tracking Data in Rubin and Wu (1997)
Biometrics. Yingnian Wu (wu@hustat.harvard.edu) [14/Oct/97]
Information about the dataset
CLASSTYPE: nominal
CLASSINDEX: last | {0: [0 - ID (numeric)],
1: [1 - target (nominal)],
2: [2 - gain_ratio_1 (numeric)],
3: [3 - gain_ratio_2 (numeric)],
4: [4 - gain_ratio_3 (numeric)],
5: [5 - gain_ratio_4 (numeric)],
6: [6 - gain_ratio_5 (numeric)],
7: [7 - gain_ratio_6 (numeric)],
8: [8 - gain_ratio_7 (numeric)],
9: [9 - gain_ratio_8 (numeric... | {'MajorityClassSize': 177.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 163.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 340.0,
'NumberOfInstancesWithMissingValues': 228.0,
'NumberOfMissingValues': 834.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 3.... | schizo | [
"ID",
"target",
"gain_ratio_1",
"gain_ratio_2",
"gain_ratio_3",
"gain_ratio_4",
"gain_ratio_5",
"gain_ratio_6",
"gain_ratio_7",
"gain_ratio_8",
"gain_ratio_9",
"gain_ratio_10",
"gain_ratio_11",
"sex"
] | [
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true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 2,588 |
146,073 | precision | precision_score | biomed | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
February 23, 1982
The 1982 annual meetings of the American Statistical Association (ASA)
will be held August 16-19, 1982 in Cincinnati. At that meeting, the ASA
Committee on Statistical Graphics plans to sponsor an "Exposition of
Statistical Gra... | {0: [0 - Observation_number (nominal)],
1: [1 - Hospital_identification_number_for_blood_sample (numeric)],
2: [2 - Age_of_patient (numeric)],
3: [3 - Date_that_blood_sample_was_taken (numeric)],
4: [4 - ml (numeric)],
5: [5 - m2 (numeric)],
6: [6 - m3 (numeric)],
7: [7 - m4 (numeric)],
8: [8 - class (nominal)]... | {'MajorityClassSize': 134.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 75.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 15.0,
'NumberOfMissingValues': 15.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 2.0,
'... | biomed | [
"Observation_number",
"Hospital_identification_number_for_blood_sample",
"Age_of_patient",
"Date_that_blood_sample_was_taken",
"ml",
"m2",
"m3",
"m4"
] | [
true,
false,
false,
false,
false,
false,
false,
false
] | 2,589 |
146,081 | precision | precision_score | teachingAssistant | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Dataset from the MLRR repository: http://axon.cs.byu.edu:5000/ | {0: [0 - ID (numeric)],
1: [1 - EnglishSepaker (nominal)],
2: [2 - courseInstructor (nominal)],
3: [3 - course (nominal)],
4: [4 - summer (nominal)],
5: [5 - classSize (numeric)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 52.0,
'MaxNominalAttDistinctValues': 26.0,
'MinorityClassSize': 49.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 151.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 5.0,
'co... | teachingAssistant | [
"ID",
"EnglishSepaker",
"courseInstructor",
"course",
"summer",
"classSize"
] | [
false,
true,
true,
true,
true,
false
] | 2,590 |
146,148 | precision | precision_score | MyIris | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
MyExampleIris | {0: [0 - sepallength (numeric)],
1: [1 - sepalwidth (numeric)],
2: [2 - petallength (numeric)],
3: [3 - petalwidth (numeric)],
4: [4 - class (nominal)]} | {'MajorityClassSize': 50.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 50.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 150.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | MyIris | [
"sepallength",
"sepalwidth",
"petallength",
"petalwidth"
] | [
false,
false,
false,
false
] | 2,592 |
145,978 | precision | precision_score | soybean | **Author**: R.S. Michalski and R.L. Chilausky (Donors: Ming Tan & Jeff Schlimmer)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Soybean+(Large)) - 1988
**Please cite**: R.S. Michalski and R.L. Chilausky "Learning by Being Told and Learning from Examples: An Experimental Comparison of the Two Methods of ... | {0: [0 - date (nominal)],
1: [1 - plant-stand (nominal)],
2: [2 - precip (nominal)],
3: [3 - temp (nominal)],
4: [4 - hail (nominal)],
5: [5 - crop-hist (nominal)],
6: [6 - area-damaged (nominal)],
7: [7 - severity (nominal)],
8: [8 - seed-tmt (nominal)],
9: [9 - germination (nominal)],
10: [10 - plant-growth... | {'MajorityClassSize': 92.0,
'MaxNominalAttDistinctValues': 19.0,
'MinorityClassSize': 8.0,
'NumberOfClasses': 19.0,
'NumberOfFeatures': 36.0,
'NumberOfInstances': 683.0,
'NumberOfInstancesWithMissingValues': 121.0,
'NumberOfMissingValues': 2337.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 36.... | soybean | [
"date",
"plant-stand",
"precip",
"temp",
"hail",
"crop-hist",
"area-damaged",
"severity",
"seed-tmt",
"germination",
"plant-growth",
"leaves",
"leafspots-halo",
"leafspots-marg",
"leafspot-size",
"leaf-shread",
"leaf-malf",
"leaf-mild",
"stem",
"lodging",
"stem-cankers",
"c... | [
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
] | 2,593 |
146,071 | precision | precision_score | SyskillWebert-Bands | **Author**: Michael Pazzani (pazzani@ics.uci.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Syskill+and+Webert+Web+Page+Ratings)- 1999
**Please cite**:
**Syskill and Webert Web Page Ratings**
This database contains the HTML source of web pages plus the ratings of a single user on these web pag... | {0: [0 - id (string)], 1: [1 - text (string)], 2: [2 - class (nominal)]} | {'MajorityClassSize': 39.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 7.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 61.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 1.0,
'cost_... | SyskillWebert-Bands | [
"id",
"text"
] | [
false,
false
] | 2,594 |
146,067 | precision | precision_score | SyskillWebert-BioMedical | **Author**: Michael Pazzani (pazzani@ics.uci.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Syskill+and+Webert+Web+Page+Ratings)- 1999
**Please cite**:
**Syskill and Webert Web Page Ratings**
This database contains the HTML source of web pages plus the ratings of a single user on these web pag... | {0: [0 - id (string)], 1: [1 - text (string)], 2: [2 - class (nominal)]} | {'MajorityClassSize': 96.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 3.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 131.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | SyskillWebert-BioMedical | [
"id",
"text"
] | [
false,
false
] | 2,595 |
75,108 | predictive_accuracy | accuracy_score | musk | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Dataset from the MLRR repository: http://axon.cs.byu.edu:5000/
More infos: https://archive.ics.uci.edu/ml/datasets/Musk+(Version+2) | {0: [0 - ID (numeric)],
1: [1 - molecule_name (nominal)],
2: [2 - conformation_name (nominal)],
3: [3 - f1 (numeric)],
4: [4 - f2 (numeric)],
5: [5 - f3 (numeric)],
6: [6 - f4 (numeric)],
7: [7 - f5 (numeric)],
8: [8 - f6 (numeric)],
9: [9 - f7 (numeric)],
10: [10 - f8 (numeric)],
11: [11 - f9 (numeric)],
1... | {'MajorityClassSize': 5581.0,
'MaxNominalAttDistinctValues': 102.0,
'MinorityClassSize': 1017.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 168.0,
'NumberOfInstances': 6598.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 166.0,
'NumberOfSymbolicFeatures':... | musk | [
"molecule_name",
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"f7",
"f8",
"f9",
"f10",
"f11",
"f12",
"f13",
"f14",
"f15",
"f16",
"f17",
"f18",
"f19",
"f20",
"f21",
"f22",
"f23",
"f24",
"f25",
"f26",
"f27",
"f28",
"f29",
"f30",
"f31",
"f32",
"f33",
"f34",
... | [
true,
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,
fa... | 2,596 |
146,084 | precision | precision_score | badges2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Dataset from the MLRR repository: http://axon.cs.byu.edu:5000/ | {0: [0 - ID (numeric)],
1: [1 - length (numeric)],
2: [2 - even_odd (nominal)],
3: [3 - first_char_vowel (nominal)],
4: [4 - second_char_vowel (nominal)],
5: [5 - vowels (numeric)],
6: [6 - consonants (numeric)],
7: [7 - vowel_consonant_ratio (numeric)],
8: [8 - spaces (numeric)],
9: [9 - dots (numeric)],
10:... | {'MajorityClassSize': 210.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 84.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 294.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 4.0,
'c... | badges2 | [
"length",
"even_odd",
"first_char_vowel",
"second_char_vowel",
"vowels",
"consonants",
"vowel_consonant_ratio",
"spaces",
"dots",
"words"
] | [
false,
true,
true,
true,
false,
false,
false,
false,
false,
false
] | 2,597 |
145,985 | precision | precision_score | waveform-5000 | **Author**: Breiman,L., Friedman,J.H., Olshen,R.A., & Stone,C.J.
**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/waveform+database+generator+(version+2)) - 1988
**Please cite**: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html)
**Waveform Database Generator**
Generator generating 3 classes o... | {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 - x11 (numeric)],
11: [11 - x12 (numeric)],
12: [12 - x13 (numeric)]... | {'MajorityClassSize': 1692.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 1653.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 41.0,
'NumberOfInstances': 5000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 40.0,
'NumberOfSymbolicFeatures': 1.0... | waveform-5000 | [
"x1",
"x2",
"x3",
"x4",
"x5",
"x6",
"x7",
"x8",
"x9",
"x10",
"x11",
"x12",
"x13",
"x14",
"x15",
"x16",
"x17",
"x18",
"x19",
"x20",
"x21",
"x22",
"x23",
"x24",
"x25",
"x26",
"x27",
"x28",
"x29",
"x30",
"x31",
"x32",
"x33",
"x34",
"x35",
"x36",
"... | [
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... | 2,598 |
146,176 | precision | precision_score | GAMETES_Epistasis_3-Way_20atts_0.2H_EDM-1_1 | GAMETES_Epistasis_3-Way_20atts_0.2H_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': 21.0,
'NumberOfInstances': 1600.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 21.0,
... | GAMETES_Epistasis_3-Way_20atts_0.2H_EDM-1_1 | [
"N0",
"N1",
"N2",
"N3",
"N4",
"N5",
"N6",
"N7",
"N8",
"N9",
"N10",
"N11",
"N12",
"N13",
"N14",
"N15",
"N16",
"P1",
"P2",
"P3"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,599 |
4,631 | predictive_accuracy | accuracy_score | rsctc2010_3 | **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': 27.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 5.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 22278.0,
'NumberOfInstances': 95.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22277.0,
'NumberOfSymbolicFeatures': 1.0,... | rsctc2010_3 | [
"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... | 2,600 |
146,175 | precision | precision_score | GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1 | GAMETES_Epistasis_2-Way_20atts_0.4H_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': 21.0,
'NumberOfInstances': 1600.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 21.0,
... | GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1 | [
"N0",
"N1",
"N2",
"N3",
"N4",
"N5",
"N6",
"N7",
"N8",
"N9",
"N10",
"N11",
"N12",
"N13",
"N14",
"N15",
"N16",
"N17",
"P1",
"P2"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,601 |
145,967 | precision | precision_score | mushroom | **Author**: [Jeff Schlimmer](Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/mushroom) - 1981
**Please cite**: The Audubon Society Field Guide to North American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred A. Knopf
### Description
This dataset descri... | {0: [0 - cap-shape (nominal)],
1: [1 - cap-surface (nominal)],
2: [2 - cap-color (nominal)],
3: [3 - bruises%3F (nominal)],
4: [4 - odor (nominal)],
5: [5 - gill-attachment (nominal)],
6: [6 - gill-spacing (nominal)],
7: [7 - gill-size (nominal)],
8: [8 - gill-color (nominal)],
9: [9 - stalk-shape (nominal)],
... | {'MajorityClassSize': 4208.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 3916.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 8124.0,
'NumberOfInstancesWithMissingValues': 2480.0,
'NumberOfMissingValues': 2480.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures... | mushroom | [
"cap-shape",
"cap-surface",
"cap-color",
"bruises%3F",
"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-col... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,602 |
75,248 | predictive_accuracy | accuracy_score | ipums_la_98-small | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (numeric)],
6: [6 - rent (numeric)],
7: [7 - ftotinc (numeric)],
8: [8 - nfams (nominal)],
9: [9 - ncouples (nominal)],
10: [10 - nmothers (nominal)],
11: [11 - nfa... | {'MajorityClassSize': 6694.0,
'MaxNominalAttDistinctValues': 15.0,
'MinorityClassSize': 791.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 56.0,
'NumberOfInstances': 7485.0,
'NumberOfInstancesWithMissingValues': 7369.0,
'NumberOfMissingValues': 32427.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeature... | ipums_la_98-small | [
"year",
"gq",
"gqtypeg",
"farm",
"ownershg",
"value",
"rent",
"ftotinc",
"nfams",
"ncouples",
"nmothers",
"nfathers",
"momloc",
"stepmom",
"momrule",
"poploc",
"steppop",
"poprule",
"sploc",
"sprule",
"famsize",
"nchild",
"nchlt5",
"famunit",
"eldch",
"yngch",
"ns... | [
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,... | 2,603 |
146,191 | precision | precision_score | calendarDOW | calendarDOW-pmlb | {0: [0 - Feature00 (numeric)],
1: [1 - Feature01 (numeric)],
2: [2 - Feature02 (nominal)],
3: [3 - Feature03 (nominal)],
4: [4 - Feature04 (numeric)],
5: [5 - Feature05 (nominal)],
6: [6 - Feature06 (numeric)],
7: [7 - Feature07 (numeric)],
8: [8 - Feature08 (nominal)],
9: [9 - Feature09 (nominal)],
10: [10 -... | {'MajorityClassSize': 96.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 44.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 399.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 21.0,
... | calendarDOW | [
"Feature00",
"Feature01",
"Feature02",
"Feature03",
"Feature04",
"Feature05",
"Feature06",
"Feature07",
"Feature08",
"Feature09",
"Feature10",
"Feature11",
"Feature12",
"Feature13",
"Feature14",
"Feature15",
"Feature16",
"Feature17",
"Feature18",
"Feature19",
"Feature20",
"... | [
false,
false,
true,
true,
false,
true,
false,
false,
true,
true,
true,
true,
false,
false,
true,
true,
true,
true,
false,
false,
true,
false,
true,
false,
true,
true,
true,
true,
true,
true,
false,
true
] | 2,604 |
146,198 | precision | precision_score | ecoli | ecoli-pmlb | {0: [0 - mcg (numeric)],
1: [1 - gvh (numeric)],
2: [2 - lip (numeric)],
3: [3 - chg (numeric)],
4: [4 - aac (numeric)],
5: [5 - alm1 (numeric)],
6: [6 - alm2 (numeric)],
7: [7 - class (nominal)]} | {'MajorityClassSize': 143.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 327.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | ecoli | [
"mcg",
"gvh",
"lip",
"chg",
"aac",
"alm1",
"alm2"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,605 |
146,196 | precision | precision_score | corral | corral-pmlb | {0: [0 - A0 (nominal)],
1: [1 - A1 (nominal)],
2: [2 - B0 (nominal)],
3: [3 - B1 (nominal)],
4: [4 - Irrelevant (nominal)],
5: [5 - Correlated (nominal)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 90.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 70.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 160.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'cos... | corral | [
"A0",
"A1",
"B0",
"B1",
"Irrelevant",
"Correlated"
] | [
true,
true,
true,
true,
true,
true
] | 2,606 |
146,188 | precision | precision_score | analcatdata_fraud | analcatdata_fraud-pmlb | {0: [0 - AC1 (nominal)],
1: [1 - AC9 (nominal)],
2: [2 - AC16 (nominal)],
3: [3 - CL7 (nominal)],
4: [4 - CL11 (nominal)],
5: [5 - IJ2 (nominal)],
6: [6 - IJ3 (nominal)],
7: [7 - IJ4 (nominal)],
8: [8 - IJ6 (nominal)],
9: [9 - IJ12 (nominal)],
10: [10 - Total (nominal)],
11: [11 - class (nominal)]} | {'MajorityClassSize': 29.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 13.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 42.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
'co... | analcatdata_fraud | [
"AC1",
"AC9",
"AC16",
"CL7",
"CL11",
"IJ2",
"IJ3",
"IJ4",
"IJ6",
"IJ12",
"Total"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,607 |
146,174 | precision | precision_score | GAMETES_Epistasis_2-Way_20atts_0.1H_EDM-1_1 | GAMETES_Epistasis_2-Way_20atts_0.1H_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': 21.0,
'NumberOfInstances': 1600.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 21.0,
... | GAMETES_Epistasis_2-Way_20atts_0.1H_EDM-1_1 | [
"N0",
"N1",
"N2",
"N3",
"N4",
"N5",
"N6",
"N7",
"N8",
"N9",
"N10",
"N11",
"N12",
"N13",
"N14",
"N15",
"N16",
"N17",
"P1",
"P2"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,608 |
146,209 | precision | precision_score | thyroid-new | new-thyroid-pmlb | {0: [0 - 2 (numeric)],
1: [1 - 3 (numeric)],
2: [2 - 4 (numeric)],
3: [3 - 5 (numeric)],
4: [4 - 6 (numeric)],
5: [5 - class (nominal)]} | {'MajorityClassSize': 150.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 30.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 215.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | thyroid-new | [
"2",
"3",
"4",
"5",
"6"
] | [
false,
false,
false,
false,
false
] | 2,611 |
146,192 | precision | precision_score | car-evaluation | car-evaluation-pmlb | {0: [0 - class (nominal)],
1: [1 - buying_price_vhigh (nominal)],
2: [2 - buying_price_high (nominal)],
3: [3 - buying_price_med (nominal)],
4: [4 - buying_price_low (nominal)],
5: [5 - maintenance_price_vhigh (nominal)],
6: [6 - maintenance_price_high (nominal)],
7: [7 - maintenance_price_med (nominal)],
8: [8... | {'MajorityClassSize': 1210.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 65.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 1728.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 22.0,
... | car-evaluation | [
"buying_price_vhigh",
"buying_price_high",
"buying_price_med",
"buying_price_low",
"maintenance_price_vhigh",
"maintenance_price_high",
"maintenance_price_med",
"maintenance_price_low",
"doors_2",
"doors_3",
"doors_4",
"doors_5more",
"persons_2",
"persons_4",
"persons_more",
"luggage_b... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,612 |
146,178 | precision | precision_score | GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_75_EDM-2_001 | GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_75_EDM-2_001-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': 21.0,
'NumberOfInstances': 1600.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 21.0,
... | GAMETES_Heterogeneity_20atts_1600_Het_0.4_0.2_75_EDM-2_001 | [
"N0",
"N1",
"N2",
"N3",
"N4",
"N5",
"N6",
"N7",
"N8",
"N9",
"N10",
"N11",
"N12",
"N13",
"N14",
"N15",
"M0P0",
"M0P1",
"M1P0",
"M1P1"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,614 |
75,244 | predictive_accuracy | accuracy_score | ipums_la_99-small | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (numeric)],
6: [6 - rent (numeric)],
7: [7 - ftotinc (numeric)],
8: [8 - nfams (nominal)],
9: [9 - ncouples (nominal)],
10: [10 - nmothers (nominal)],
11: [11 - nfa... | {'MajorityClassSize': 8276.0,
'MaxNominalAttDistinctValues': 17.0,
'MinorityClassSize': 568.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 57.0,
'NumberOfInstances': 8844.0,
'NumberOfInstancesWithMissingValues': 8844.0,
'NumberOfMissingValues': 34843.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeature... | ipums_la_99-small | [
"year",
"gq",
"gqtypeg",
"farm",
"ownershg",
"value",
"rent",
"ftotinc",
"nfams",
"ncouples",
"nmothers",
"nfathers",
"momloc",
"stepmom",
"momrule",
"poploc",
"steppop",
"poprule",
"sploc",
"sprule",
"famsize",
"nchild",
"nchlt5",
"famunit",
"eldch",
"yngch",
"ns... | [
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,... | 2,615 |
146,219 | precision | precision_score | xd6 | **Author**: Unknown
**Source**: [PMLB](https://github.com/EpistasisLab/penn-ml-benchmarks/tree/master/datasets/classification) - Supposedly originates from UCI, but can't find it there anymore.
**Please cite:**
**XD6 Dataset**
Dataset used by Buntine and Niblett (1992). Composed of 10 features, one of which is i... | {0: [0 - Attribute_1 (nominal)],
1: [1 - Attribute_2 (nominal)],
2: [2 - Attribute_3 (nominal)],
3: [3 - Attribute_4 (nominal)],
4: [4 - Attribute_5 (nominal)],
5: [5 - Attribute_6 (nominal)],
6: [6 - Attribute_7 (nominal)],
7: [7 - Attribute_8 (nominal)],
8: [8 - Attribute_9 (nominal)],
9: [9 - class (nominal... | {'MajorityClassSize': 651.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 322.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 973.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 10.0,
... | xd6 | [
"Attribute_1",
"Attribute_2",
"Attribute_3",
"Attribute_4",
"Attribute_5",
"Attribute_6",
"Attribute_7",
"Attribute_8",
"Attribute_9"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,616 |
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