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 |
|---|---|---|---|---|---|---|---|---|---|---|
189,767 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 818 |
5,093 | mean_absolute_error | mean_absolute_error | yprop_4_1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This is one of 41 drug design datasets. The datasets with 1143 features are formed using Adriana.Code software (www.molecular-networks.com/software/adrianacode).
The molecules and outputs are taken from the original studies (see below). The other... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 252.0,
'NumberOfInstances': 8885.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 252.0,
'NumberOfSymbolicFeatures': 0.0,
'... | yprop_4_1 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
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f... | 819 |
167,210 | root_mean_squared_error | root_mean_squared_error | Moneyball | **Author**: MITx
**Source**: [Kaggle](https://www.kaggle.com/wduckett/moneyball-mlb-stats-19622012/data), originally from [The Analytics Edge course on EdX](https://www.edx.org/course/analytics-edge-mitx-15-071x-3). Data collected from [baseball-reference.com](baseball-reference.com)
**Please cite**:
**Moneybal... | {0: [0 - Team (nominal)],
1: [1 - League (nominal)],
2: [2 - Year (numeric)],
3: [3 - RS (numeric)],
4: [4 - RA (numeric)],
5: [5 - W (numeric)],
6: [6 - OBP (numeric)],
7: [7 - SLG (numeric)],
8: [8 - BA (numeric)],
9: [9 - Playoffs (nominal)],
10: [10 - RankSeason (nominal)],
11: [11 - RankPlayoffs (nomina... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 39.0,
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'NumberOfClasses': 0.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 1232.0,
'NumberOfInstancesWithMissingValues': 1118.0,
'NumberOfMissingValues': 3600.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 6.0... | Moneyball | [
"Team",
"League",
"Year",
"RA",
"W",
"OBP",
"SLG",
"BA",
"Playoffs",
"RankSeason",
"RankPlayoffs",
"G",
"OOBP",
"OSLG"
] | [
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
false,
false
] | 820 |
189,764 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 821 |
189,768 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 822 |
189,769 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 823 |
211,693 | root_mean_squared_error | root_mean_squared_error | detroit | **Author**:
**Source**: Unknown -
**Please cite**:
Data from StatLib (ftp stat.cmu.edu/datasets)
This is the data set called `DETROIT' in the book `Subset selection in
regression' by Alan J. Miller published in the Chapman & Hall series of
monographs on Statistics & Applied Probability, no. 40. The data... | {0: [0 - FTP (numeric)],
1: [1 - UEMP (numeric)],
2: [2 - MAN (numeric)],
3: [3 - LIC (numeric)],
4: [4 - GR (numeric)],
5: [5 - CLEAR (numeric)],
6: [6 - WM (numeric)],
7: [7 - NMAN (numeric)],
8: [8 - GOV (numeric)],
9: [9 - HE (numeric)],
10: [10 - WE (numeric)],
11: [11 - HOM (numeric)],
12: [12 - ACC (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 13.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | detroit | [
"FTP",
"UEMP",
"MAN",
"LIC",
"GR",
"CLEAR",
"WM",
"NMAN",
"GOV",
"HE",
"WE",
"ACC",
"ASR"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 824 |
166,852 | predictive_accuracy | accuracy_score | Moneyball | **Author**: MITx
**Source**: [Kaggle](https://www.kaggle.com/wduckett/moneyball-mlb-stats-19622012/data), originally from [The Analytics Edge course on EdX](https://www.edx.org/course/analytics-edge-mitx-15-071x-3). Data collected from [baseball-reference.com](baseball-reference.com)
**Please cite**:
**Moneybal... | {0: [0 - Team (nominal)],
1: [1 - League (nominal)],
2: [2 - Year (numeric)],
3: [3 - RS (numeric)],
4: [4 - RA (numeric)],
5: [5 - W (numeric)],
6: [6 - OBP (numeric)],
7: [7 - SLG (numeric)],
8: [8 - BA (numeric)],
9: [9 - Playoffs (nominal)],
10: [10 - RankSeason (nominal)],
11: [11 - RankPlayoffs (nomina... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 39.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 1232.0,
'NumberOfInstancesWithMissingValues': 1118.0,
'NumberOfMissingValues': 3600.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 6.0... | Moneyball | [
"Team",
"League",
"Year",
"RA",
"W",
"OBP",
"SLG",
"BA",
"Playoffs",
"RankSeason",
"RankPlayoffs",
"G",
"OOBP",
"OSLG"
] | [
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
false,
false
] | 825 |
190,423 | root_mean_squared_error | root_mean_squared_error | Concrete_Data | Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. | {0: [0 - Cement (component 1)(kg in a m^3 mixture) (numeric)],
1: [1 - Blast Furnace Slag (component 2)(kg in a m^3 mixture) (numeric)],
2: [2 - Fly Ash (component 3)(kg in a m^3 mixture) (numeric)],
3: [3 - Water (component 4)(kg in a m^3 mixture) (numeric)],
4: [4 - Superplasticizer (component 5)(kg in a m^3 mix... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 1030.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Concrete_Data | [
"Cement (component 1)(kg in a m^3 mixture)",
"Blast Furnace Slag (component 2)(kg in a m^3 mixture)",
"Fly Ash (component 3)(kg in a m^3 mixture)",
"Water (component 4)(kg in a m^3 mixture)",
"Superplasticizer (component 5)(kg in a m^3 mixture)",
"Coarse Aggregate (component 6)(kg in a m^3 mixture)",
... | [
false,
false,
false,
false,
false,
false,
false,
false
] | 826 |
211,690 | root_mean_squared_error | root_mean_squared_error | liver-disorders | **Author**: BUPA Medical Research Ltd. Donor: Richard S. Forsyth
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Liver+Disorders) - 5/15/1990
**Please cite**:
**BUPA liver disorders**
The first 5 variables are all blood tests which are thought to be sensitive to liver disorders that might arise from ... | {0: [0 - mcv (numeric)],
1: [1 - alkphos (numeric)],
2: [2 - sgpt (numeric)],
3: [3 - sgot (numeric)],
4: [4 - gammagt (numeric)],
5: [5 - drinks (numeric)],
6: [6 - selector (nominal)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 345.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | liver-disorders | [
"mcv",
"alkphos",
"sgpt",
"sgot",
"gammagt"
] | [
false,
false,
false,
false,
false
] | 827 |
14,950 | predictive_accuracy | accuracy_score | TurkiyeStudentEvaluation | Abstract: This data set contains a total 5820 evaluation scores provided by students from Gazi University in Ankara (Turkey). There is a total of 28 course specific questions and additional 5 attributes.
Source:
Ernest Fokoue
Center for Quality and Applied Statistics
Rochester Institute of Technology
98 Lomb Memori... | {0: [0 - instr (numeric)],
1: [1 - class (numeric)],
2: [2 - nb.repeat (numeric)],
3: [3 - attendance (numeric)],
4: [4 - difficulty (numeric)],
5: [5 - Q1 (numeric)],
6: [6 - Q2 (numeric)],
7: [7 - Q3 (numeric)],
8: [8 - Q4 (numeric)],
9: [9 - Q5 (numeric)],
10: [10 - Q6 (numeric)],
11: [11 - Q7 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 5820.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | TurkiyeStudentEvaluation | [
"instr",
"nb.repeat",
"attendance",
"difficulty",
"Q1",
"Q2",
"Q3",
"Q4",
"Q5",
"Q6",
"Q7",
"Q8",
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"Q19",
"Q20",
"Q21",
"Q22",
"Q23",
"Q24",
"Q25",
"Q26",
"Q27",
"Q28"
] | [
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] | 828 |
5,495 | predictive_accuracy | accuracy_score | satellite_image | **Author**:
**Source**: Unknown - 1993
**Please cite**:
Source:
Ashwin Srinivasan
Department of Statistics and Data Modeling
University of Strathclyde
Glasgow
Scotland
UK
ross '@' uk.ac.turing
The original Landsat data for this database was generated from data purchased from NASA by the Australian Centre for ... | {0: [0 - attr1 (numeric)],
1: [1 - attr2 (numeric)],
2: [2 - attr3 (numeric)],
3: [3 - attr4 (numeric)],
4: [4 - attr5 (numeric)],
5: [5 - attr6 (numeric)],
6: [6 - attr7 (numeric)],
7: [7 - attr8 (numeric)],
8: [8 - attr9 (numeric)],
9: [9 - attr10 (numeric)],
10: [10 - attr11 (numeric)],
11: [11 - attr12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 6435.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 37.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | satellite_image | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6",
"attr7",
"attr8",
"attr9",
"attr10",
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f... | 829 |
211,692 | root_mean_squared_error | root_mean_squared_error | analcatdata_negotiation | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Role (nominal)],
1: [1 - Status (numeric)],
2: [2 - Trust (numeric)],
3: [3 - Outcome_favorability (numeric)],
4: [4 - Procedural_fairness (numeric)],
5: [5 - Future_business (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 92.0,
'NumberOfInstancesWithMissingValues': 17.0,
'NumberOfMissingValues': 26.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | analcatdata_negotiation | [
"Role",
"Status",
"Trust",
"Outcome_favorability",
"Procedural_fairness"
] | [
true,
false,
false,
false,
false
] | 831 |
211,725 | predictive_accuracy | accuracy_score | autoMpg | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Identifier attribute deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
i... | {0: [0 - cylinders (nominal)],
1: [1 - displacement (numeric)],
2: [2 - horsepower (numeric)],
3: [3 - weight (numeric)],
4: [4 - acceleration (numeric)],
5: [5 - model (nominal)],
6: [6 - origin (nominal)],
7: [7 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 13.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 398.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 6.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 3.0,
'cost... | autoMpg | [
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
"model",
"origin"
] | [
true,
false,
false,
false,
false,
true,
true
] | 833 |
212,052 | predictive_accuracy | accuracy_score | duke-breast-cancer | **Author**: Shirish Krishnaj Shevade and S. Sathiya Keerthi.
libSVM","AAD group
**Source**: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html) - Date unknown
**Please cite**: Shirish Krishnaj Shevade and S. Sathiya Keerthi.
A simple and efficient algorithm for gene selection using spar... | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7130.0,
'NumberOfInstances': 86.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7130.0,
'NumberOfSymbolicFeatures': 0.0,
'... | duke-breast-cancer | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 834 |
189,931 | predictive_accuracy | accuracy_score | CPMP-2015-regression | source: An Algorithm Selection Benchmark for the Container Pre-Marshalling Problem (CPMP)
authors: K. Tierney and Y. Malitsky (features) / K. Tierney and D. Pacino and S. Voss (algorithms)
translator in coseal format: K. Tierney
This is an extension of the 2013 premarshalling dataset that includes more features and a ... | {0: [0 - instance_id (string)],
1: [1 - repetition (numeric)],
2: [2 - stacks (numeric)],
3: [3 - tiers (numeric)],
4: [4 - stack.tier.ratio (numeric)],
5: [5 - container.density (numeric)],
6: [6 - empty.stack.pct (numeric)],
7: [7 - overstowing.stack.pct (numeric)],
8: [8 - overstowing.2cont.stack.pct (numeri... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 27.0,
'NumberOfInstances': 2108.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 24.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | CPMP-2015-regression | [
"instance_id",
"repetition",
"stacks",
"tiers",
"stack.tier.ratio",
"container.density",
"empty.stack.pct",
"overstowing.stack.pct",
"overstowing.2cont.stack.pct",
"group.same.min",
"group.same.max",
"group.same.mean",
"group.same.stdev",
"top.good.min",
"top.good.max",
"top.good.mean"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true
] | 835 |
211,730 | predictive_accuracy | accuracy_score | pharynx | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Case number deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
in Connec... | {0: [0 - Inst (nominal)],
1: [1 - sex (nominal)],
2: [2 - Treatment (nominal)],
3: [3 - Grade (nominal)],
4: [4 - Age (numeric)],
5: [5 - Condition (nominal)],
6: [6 - Site (nominal)],
7: [7 - T (nominal)],
8: [8 - N (nominal)],
9: [9 - Entry (nominal)],
10: [10 - Status (nominal)],
11: [11 - class (numeric)... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 184.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 195.0,
'NumberOfInstancesWithMissingValues': 2.0,
'NumberOfMissingValues': 2.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 9.0,
'co... | pharynx | [
"Inst",
"sex",
"Treatment",
"Grade",
"Age",
"Condition",
"Site",
"T",
"N",
"Status"
] | [
true,
true,
true,
true,
false,
true,
true,
true,
true,
true
] | 836 |
211,695 | root_mean_squared_error | root_mean_squared_error | cpu | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Attributes 2 and 8 deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Prog... | {0: [0 - vendor (nominal)],
1: [1 - MYCT (numeric)],
2: [2 - MMIN (numeric)],
3: [3 - MMAX (numeric)],
4: [4 - CACH (numeric)],
5: [5 - CHMIN (numeric)],
6: [6 - CHMAX (numeric)],
7: [7 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 30.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | cpu | [
"vendor",
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
true,
false,
false,
false,
false,
false,
false
] | 837 |
211,729 | predictive_accuracy | accuracy_score | autoPrice | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
All nominal attributes and instances with missing values are deleted.
Price treated as the class attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric predictio... | {0: [0 - symboling (numeric)],
1: [1 - normalized-losses (numeric)],
2: [2 - wheel-base (numeric)],
3: [3 - length (numeric)],
4: [4 - width (numeric)],
5: [5 - height (numeric)],
6: [6 - curb-weight (numeric)],
7: [7 - engine-size (numeric)],
8: [8 - bore (numeric)],
9: [9 - stroke (numeric)],
10: [10 - comp... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | autoPrice | [
"symboling",
"normalized-losses",
"wheel-base",
"length",
"width",
"height",
"curb-weight",
"engine-size",
"bore",
"stroke",
"compression-ratio",
"horsepower",
"peak-rpm",
"city-mpg",
"highway-mpg"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 838 |
211,694 | root_mean_squared_error | root_mean_squared_error | bodyfat | **Author**: Roger W. Johnson
**Source**: [UCI (not available anymore)](https://archive.ics.uci.edu/ml/index.php), [TunedIT](http://tunedit.org/repo/UCI/numeric/bodyfat.arff)
**Please cite**: None.
Short Summary:
Lists estimates of the percentage of body fat determined by underwater
weighing and various body circu... | {0: [0 - Density (numeric)],
1: [1 - Age (numeric)],
2: [2 - Weight (numeric)],
3: [3 - Height (numeric)],
4: [4 - Neck (numeric)],
5: [5 - Chest (numeric)],
6: [6 - Abdomen (numeric)],
7: [7 - Hip (numeric)],
8: [8 - Thigh (numeric)],
9: [9 - Knee (numeric)],
10: [10 - Ankle (numeric)],
11: [11 - Biceps (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 252.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | bodyfat | [
"Density",
"Age",
"Weight",
"Height",
"Neck",
"Chest",
"Abdomen",
"Hip",
"Thigh",
"Knee",
"Ankle",
"Biceps",
"Forearm",
"Wrist"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 839 |
211,691 | root_mean_squared_error | root_mean_squared_error | auto_price | **Author**:
**Source**: Unknown -
**Please cite**:
This data set consists of three types of entities:
(a) the specification of an auto in terms of various characteristics;
(b) its assigned insurance risk rating,;
(c) its normalized losses in use as compared to other cars.
The second rating corresponds to... | {0: [0 - symboling (nominal)],
1: [1 - normalized-losses (numeric)],
2: [2 - wheel-base (numeric)],
3: [3 - length (numeric)],
4: [4 - width (numeric)],
5: [5 - height (numeric)],
6: [6 - curb-weight (numeric)],
7: [7 - engine-size (numeric)],
8: [8 - bore (numeric)],
9: [9 - stroke (numeric)],
10: [10 - comp... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | auto_price | [
"symboling",
"normalized-losses",
"wheel-base",
"length",
"width",
"height",
"curb-weight",
"engine-size",
"bore",
"stroke",
"compression-ratio",
"horsepower",
"peak-rpm",
"city-mpg",
"highway-mpg"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 840 |
146,895 | predictive_accuracy | accuracy_score | TurkiyeStudentEvaluation | Abstract: This data set contains a total 5820 evaluation scores provided by students from Gazi University in Ankara (Turkey). There is a total of 28 course specific questions and additional 5 attributes.
Source:
Ernest Fokoue
Center for Quality and Applied Statistics
Rochester Institute of Technology
98 Lomb Memori... | {0: [0 - instr (numeric)],
1: [1 - class (numeric)],
2: [2 - nb.repeat (numeric)],
3: [3 - attendance (numeric)],
4: [4 - difficulty (numeric)],
5: [5 - Q1 (numeric)],
6: [6 - Q2 (numeric)],
7: [7 - Q3 (numeric)],
8: [8 - Q4 (numeric)],
9: [9 - Q5 (numeric)],
10: [10 - Q6 (numeric)],
11: [11 - Q7 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 5820.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | TurkiyeStudentEvaluation | [
"instr",
"nb.repeat",
"attendance",
"difficulty",
"Q1",
"Q2",
"Q3",
"Q4",
"Q5",
"Q6",
"Q7",
"Q8",
"Q9",
"Q10",
"Q11",
"Q12",
"Q13",
"Q14",
"Q15",
"Q16",
"Q17",
"Q18",
"Q19",
"Q20",
"Q21",
"Q22",
"Q23",
"Q24",
"Q25",
"Q26",
"Q27",
"Q28"
] | [
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
] | 841 |
211,726 | predictive_accuracy | accuracy_score | fruitfly | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Identifier attribute deleted.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
NAME: Sexual activity and the lifespan of male fruitflies
TYPE: Designed (almost factorial) experiment
SIZE: 125 observations, 5 variables
DESCRI... | {0: [0 - PARTNERS (nominal)],
1: [1 - TYPE (nominal)],
2: [2 - THORAX (numeric)],
3: [3 - SLEEP (numeric)],
4: [4 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 125.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 2.0,
'cost_... | fruitfly | [
"PARTNERS",
"TYPE",
"THORAX",
"SLEEP"
] | [
true,
true,
false,
false
] | 842 |
168,737 | predictive_accuracy | accuracy_score | Students | Students | {0: [0 - instr (numeric)],
1: [1 - class (numeric)],
2: [2 - nb.repeat (numeric)],
3: [3 - attendance (numeric)],
4: [4 - difficulty (numeric)],
5: [5 - Q1 (numeric)],
6: [6 - Q2 (numeric)],
7: [7 - Q3 (numeric)],
8: [8 - Q4 (numeric)],
9: [9 - Q5 (numeric)],
10: [10 - Q6 (numeric)],
11: [11 - Q7 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 5820.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Students | [
"instr",
"nb.repeat",
"attendance",
"difficulty",
"Q1",
"Q2",
"Q3",
"Q4",
"Q5",
"Q6",
"Q7",
"Q8",
"Q9",
"Q10",
"Q11",
"Q12",
"Q13",
"Q14",
"Q15",
"Q16",
"Q17",
"Q18",
"Q19",
"Q20",
"Q21",
"Q22",
"Q23",
"Q24",
"Q25",
"Q26",
"Q27",
"Q28"
] | [
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
] | 843 |
211,735 | predictive_accuracy | accuracy_score | fishcatch | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Weight treated as the class attribute. Identifier deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding len... | {0: [0 - Species (nominal)],
1: [1 - Length1 (numeric)],
2: [2 - Length2 (numeric)],
3: [3 - Length3 (numeric)],
4: [4 - Height (numeric)],
5: [5 - Width (numeric)],
6: [6 - Sex (nominal)],
7: [7 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 158.0,
'NumberOfInstancesWithMissingValues': 87.0,
'NumberOfMissingValues': 87.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | fishcatch | [
"Species",
"Length1",
"Length2",
"Length3",
"Height",
"Width",
"Sex"
] | [
true,
false,
false,
false,
false,
false,
true
] | 844 |
190,420 | quality | accuracy_score | Wine | Test file for ML training | {0: [0 - fixed acidity (numeric)],
1: [1 - volatile acidity (numeric)],
2: [2 - citric acid (numeric)],
3: [3 - residual sugar (numeric)],
4: [4 - chlorides (numeric)],
5: [5 - free sulfur dioxide (numeric)],
6: [6 - total sulfur dioxide (numeric)],
7: [7 - density (numeric)],
8: [8 - pH (numeric)],
9: [9 - su... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1599.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Wine | [
"fixed acidity",
"volatile acidity",
"citric acid",
"residual sugar",
"chlorides",
"free sulfur dioxide",
"total sulfur dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 845 |
211,728 | predictive_accuracy | accuracy_score | lowbwt | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Identification code deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
i... | {0: [0 - LOW (nominal)],
1: [1 - AGE (numeric)],
2: [2 - LWT (numeric)],
3: [3 - RACE (nominal)],
4: [4 - SMOKE (nominal)],
5: [5 - PTL (nominal)],
6: [6 - HT (nominal)],
7: [7 - UI (nominal)],
8: [8 - FTV (nominal)],
9: [9 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 189.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 7.0,
'cost... | lowbwt | [
"LOW",
"AGE",
"LWT",
"RACE",
"SMOKE",
"PTL",
"HT",
"UI",
"FTV"
] | [
true,
false,
false,
true,
true,
true,
true,
true,
true
] | 846 |
211,727 | predictive_accuracy | accuracy_score | pbc | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Case number deleted. X treated as the class attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length ... | {0: [0 - D (nominal)],
1: [1 - Z1 (nominal)],
2: [2 - Z2 (numeric)],
3: [3 - Z3 (nominal)],
4: [4 - Z4 (nominal)],
5: [5 - Z5 (nominal)],
6: [6 - Z6 (nominal)],
7: [7 - Z7 (nominal)],
8: [8 - Z8 (numeric)],
9: [9 - Z9 (numeric)],
10: [10 - Z10 (numeric)],
11: [11 - Z11 (numeric)],
12: [12 - Z12 (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 418.0,
'NumberOfInstancesWithMissingValues': 142.0,
'NumberOfMissingValues': 1239.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 8.0,
... | pbc | [
"D",
"Z1",
"Z2",
"Z3",
"Z4",
"Z5",
"Z6",
"Z7",
"Z8",
"Z9",
"Z10",
"Z11",
"Z12",
"Z13",
"Z14",
"Z15",
"Z16",
"Z17"
] | [
true,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 847 |
189,940 | mean_absolute_error | mean_absolute_error | CPMP-2015-regression | source: An Algorithm Selection Benchmark for the Container Pre-Marshalling Problem (CPMP)
authors: K. Tierney and Y. Malitsky (features) / K. Tierney and D. Pacino and S. Voss (algorithms)
translator in coseal format: K. Tierney
This is an extension of the 2013 premarshalling dataset that includes more features and a ... | {0: [0 - instance_id (string)],
1: [1 - repetition (numeric)],
2: [2 - stacks (numeric)],
3: [3 - tiers (numeric)],
4: [4 - stack.tier.ratio (numeric)],
5: [5 - container.density (numeric)],
6: [6 - empty.stack.pct (numeric)],
7: [7 - overstowing.stack.pct (numeric)],
8: [8 - overstowing.2cont.stack.pct (numeri... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 27.0,
'NumberOfInstances': 2108.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 24.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | CPMP-2015-regression | [
"instance_id",
"repetition",
"stacks",
"tiers",
"stack.tier.ratio",
"container.density",
"empty.stack.pct",
"overstowing.stack.pct",
"overstowing.2cont.stack.pct",
"group.same.min",
"group.same.max",
"group.same.mean",
"group.same.stdev",
"top.good.min",
"top.good.max",
"top.good.mean"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true
] | 848 |
211,734 | predictive_accuracy | accuracy_score | machine_cpu | **Author**:
**Source**: Unknown -
**Please cite**:
The problem concerns Relative CPU Performance Data. More information can be obtained in the UCI Machine
Learning repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).
The used attributes are :
MYCT: machine cycle time in nanoseconds (integer)
MMIN: ... | {0: [0 - MYCT (numeric)],
1: [1 - MMIN (numeric)],
2: [2 - MMAX (numeric)],
3: [3 - CACH (numeric)],
4: [4 - CHMIN (numeric)],
5: [5 - CHMAX (numeric)],
6: [6 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | machine_cpu | [
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
false,
false,
false,
false,
false,
false
] | 849 |
211,731 | predictive_accuracy | accuracy_score | echoMonths | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Survival treated as the class attribute
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In P... | {0: [0 - still_alive (nominal)],
1: [1 - age (numeric)],
2: [2 - pericardial (nominal)],
3: [3 - fractional (numeric)],
4: [4 - epss (numeric)],
5: [5 - lvdd (numeric)],
6: [6 - wall_score (numeric)],
7: [7 - wall_index (numeric)],
8: [8 - alive_at_1 (nominal)],
9: [9 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 69.0,
'NumberOfMissingValues': 97.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | echoMonths | [
"still_alive",
"age",
"pericardial",
"fractional",
"epss",
"lvdd",
"wall_score",
"wall_index",
"alive_at_1"
] | [
true,
false,
true,
false,
false,
false,
false,
false,
true
] | 850 |
211,733 | predictive_accuracy | accuracy_score | pwLinear | **Author**:
**Source**: Unknown -
**Please cite**:
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
in Connectionist-Based Information Systems. Singapore: Springer-Verlag. | {0: [0 - a1 (numeric)],
1: [1 - a2 (numeric)],
2: [2 - a3 (numeric)],
3: [3 - a4 (numeric)],
4: [4 - a5 (numeric)],
5: [5 - a6 (numeric)],
6: [6 - a7 (numeric)],
7: [7 - a8 (numeric)],
8: [8 - a9 (numeric)],
9: [9 - a10 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 200.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | pwLinear | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"a7",
"a8",
"a9",
"a10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 851 |
211,696 | root_mean_squared_error | root_mean_squared_error | meta | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
1. Title: meta-data
2. Sources:
(a) Creator:
LIACC - University of Porto
R.Campo Alegre 823
4150 PORTO
(b) Donor: P.B.Brazdil or J.Gama Tel.: +351 600 1672
LIACC, University of Porto Fax.: +351 600 3654
Rua Campo Alegre... | {0: [0 - DS_Name (nominal)],
1: [1 - T (numeric)],
2: [2 - N (numeric)],
3: [3 - p (numeric)],
4: [4 - k (numeric)],
5: [5 - Bin (numeric)],
6: [6 - Cost (numeric)],
7: [7 - SDratio (numeric)],
8: [8 - correl (numeric)],
9: [9 - cancor1 (numeric)],
10: [10 - cancor2 (numeric)],
11: [11 - fract1 (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 24.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 528.0,
'NumberOfInstancesWithMissingValues': 264.0,
'NumberOfMissingValues': 504.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 2.0,
... | meta | [
"DS_Name",
"T",
"N",
"p",
"k",
"Bin",
"Cost",
"SDratio",
"correl",
"cancor1",
"cancor2",
"fract1",
"fract2",
"skewness",
"kurtosis",
"Hc",
"Hx",
"MCx",
"EnAtr",
"NSRatio",
"Alg_Name"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 852 |
211,757 | predictive_accuracy | accuracy_score | cpu | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Attributes 2 and 8 deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Prog... | {0: [0 - vendor (nominal)],
1: [1 - MYCT (numeric)],
2: [2 - MMIN (numeric)],
3: [3 - MMAX (numeric)],
4: [4 - CACH (numeric)],
5: [5 - CHMIN (numeric)],
6: [6 - CHMAX (numeric)],
7: [7 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 30.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | cpu | [
"vendor",
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
true,
false,
false,
false,
false,
false,
false
] | 853 |
211,732 | predictive_accuracy | accuracy_score | breastTumor | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Tumor-size treated as the class attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. I... | {0: [0 - age (numeric)],
1: [1 - menopause (nominal)],
2: [2 - inv-nodes (nominal)],
3: [3 - node-caps (nominal)],
4: [4 - deg-malig (nominal)],
5: [5 - breast (nominal)],
6: [6 - breast-quad (nominal)],
7: [7 - irradiation (nominal)],
8: [8 - recurrence (nominal)],
9: [9 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 18.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 286.0,
'NumberOfInstancesWithMissingValues': 9.0,
'NumberOfMissingValues': 9.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 8.0,
'cos... | breastTumor | [
"age",
"menopause",
"inv-nodes",
"node-caps",
"deg-malig",
"breast",
"breast-quad",
"irradiation",
"recurrence"
] | [
false,
true,
true,
true,
true,
true,
true,
true,
true
] | 854 |
211,770 | predictive_accuracy | accuracy_score | cpu.with.vendor | null | {0: [0 - vendor (nominal)],
1: [1 - MYCT (numeric)],
2: [2 - MMIN (numeric)],
3: [3 - MMAX (numeric)],
4: [4 - CACH (numeric)],
5: [5 - CHMIN (numeric)],
6: [6 - CHMAX (numeric)],
7: [7 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 30.0,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | cpu.with.vendor | [
"vendor",
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
true,
false,
false,
false,
false,
false,
false
] | 855 |
211,756 | predictive_accuracy | accuracy_score | bodyfat | **Author**: Roger W. Johnson
**Source**: [UCI (not available anymore)](https://archive.ics.uci.edu/ml/index.php), [TunedIT](http://tunedit.org/repo/UCI/numeric/bodyfat.arff)
**Please cite**: None.
Short Summary:
Lists estimates of the percentage of body fat determined by underwater
weighing and various body circu... | {0: [0 - Density (numeric)],
1: [1 - Age (numeric)],
2: [2 - Weight (numeric)],
3: [3 - Height (numeric)],
4: [4 - Neck (numeric)],
5: [5 - Chest (numeric)],
6: [6 - Abdomen (numeric)],
7: [7 - Hip (numeric)],
8: [8 - Thigh (numeric)],
9: [9 - Knee (numeric)],
10: [10 - Ankle (numeric)],
11: [11 - Biceps (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 252.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | bodyfat | [
"Density",
"Age",
"Weight",
"Height",
"Neck",
"Chest",
"Abdomen",
"Hip",
"Thigh",
"Knee",
"Ankle",
"Biceps",
"Forearm",
"Wrist"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 856 |
211,738 | predictive_accuracy | accuracy_score | libras_move | **Author**: Daniel Baptista Dias, Sarajane Marques Peres, Helton Hideraldo Biscaro
University of Sao Paulo, School of Art, Sciences and Humanities, Sao Paulo, SP, Brazil
**Source**: Unknown - November 2008
**Please cite**:
### LIBRAS Movement Database
LIBRAS, acronym of the Portuguese name "LIngua BRAsileira ... | {0: [0 - xcoord1 (numeric)],
1: [1 - ycoord1 (numeric)],
2: [2 - xcoord2 (numeric)],
3: [3 - ycoord2 (numeric)],
4: [4 - xcoord3 (numeric)],
5: [5 - ycoord3 (numeric)],
6: [6 - xcoord4 (numeric)],
7: [7 - ycoord4 (numeric)],
8: [8 - xcoord5 (numeric)],
9: [9 - ycoord5 (numeric)],
10: [10 - xcoord6 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 91.0,
'NumberOfInstances': 360.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 91.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | libras_move | [
"xcoord1",
"ycoord1",
"xcoord2",
"ycoord2",
"xcoord3",
"ycoord3",
"xcoord4",
"ycoord4",
"xcoord5",
"ycoord5",
"xcoord6",
"ycoord6",
"xcoord7",
"ycoord7",
"xcoord8",
"ycoord8",
"xcoord9",
"ycoord9",
"xcoord10",
"ycoord10",
"xcoord11",
"ycoord11",
"xcoord12",
"ycoord12",
... | [
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... | 857 |
211,759 | predictive_accuracy | accuracy_score | auto93 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Attributes 2,4, and 6 deleted. Midrange price treated as the class
attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-base... | {0: [0 - Manufacturer (nominal)],
1: [1 - Type (nominal)],
2: [2 - City_MPG (numeric)],
3: [3 - Highway_MPG (numeric)],
4: [4 - Air_Bags_standard (nominal)],
5: [5 - Drive_train_type (nominal)],
6: [6 - Number_of_cylinders (numeric)],
7: [7 - Engine_size (numeric)],
8: [8 - Horsepower (numeric)],
9: [9 - RPM (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 31.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 93.0,
'NumberOfInstancesWithMissingValues': 11.0,
'NumberOfMissingValues': 14.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 6.0,
'c... | auto93 | [
"Manufacturer",
"Type",
"City_MPG",
"Highway_MPG",
"Air_Bags_standard",
"Drive_train_type",
"Number_of_cylinders",
"Engine_size",
"Horsepower",
"RPM",
"Engine_revolutions_per_mile",
"Manual_transmission_available",
"Fuel_tank_capacity",
"Passenger_capacity",
"Length",
"Wheelbase",
"W... | [
true,
true,
false,
false,
true,
true,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 858 |
211,817 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 859 |
211,816 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 860 |
211,818 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 861 |
211,821 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 862 |
211,819 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 863 |
211,839 | mean_absolute_error | mean_absolute_error | cpu.with.vendor | null | {0: [0 - vendor (nominal)],
1: [1 - MYCT (numeric)],
2: [2 - MMIN (numeric)],
3: [3 - MMAX (numeric)],
4: [4 - CACH (numeric)],
5: [5 - CHMIN (numeric)],
6: [6 - CHMAX (numeric)],
7: [7 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 30.0,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | cpu.with.vendor | [
"vendor",
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
true,
false,
false,
false,
false,
false,
false
] | 865 |
211,837 | mean_absolute_error | mean_absolute_error | liver-disorders | **Author**: BUPA Medical Research Ltd. Donor: Richard S. Forsyth
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Liver+Disorders) - 5/15/1990
**Please cite**:
**BUPA liver disorders**
The first 5 variables are all blood tests which are thought to be sensitive to liver disorders that might arise from ... | {0: [0 - mcv (numeric)],
1: [1 - alkphos (numeric)],
2: [2 - sgpt (numeric)],
3: [3 - sgot (numeric)],
4: [4 - gammagt (numeric)],
5: [5 - drinks (numeric)],
6: [6 - selector (nominal)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 345.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | liver-disorders | [
"mcv",
"alkphos",
"sgpt",
"sgot",
"gammagt"
] | [
false,
false,
false,
false,
false
] | 866 |
211,758 | predictive_accuracy | accuracy_score | meta | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
1. Title: meta-data
2. Sources:
(a) Creator:
LIACC - University of Porto
R.Campo Alegre 823
4150 PORTO
(b) Donor: P.B.Brazdil or J.Gama Tel.: +351 600 1672
LIACC, University of Porto Fax.: +351 600 3654
Rua Campo Alegre... | {0: [0 - DS_Name (nominal)],
1: [1 - T (numeric)],
2: [2 - N (numeric)],
3: [3 - p (numeric)],
4: [4 - k (numeric)],
5: [5 - Bin (numeric)],
6: [6 - Cost (numeric)],
7: [7 - SDratio (numeric)],
8: [8 - correl (numeric)],
9: [9 - cancor1 (numeric)],
10: [10 - cancor2 (numeric)],
11: [11 - fract1 (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 24.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 528.0,
'NumberOfInstancesWithMissingValues': 264.0,
'NumberOfMissingValues': 504.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 2.0,
... | meta | [
"DS_Name",
"T",
"N",
"p",
"k",
"Bin",
"Cost",
"SDratio",
"correl",
"cancor1",
"cancor2",
"fract1",
"fract2",
"skewness",
"kurtosis",
"Hc",
"Hx",
"MCx",
"EnAtr",
"NSRatio",
"Alg_Name"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 868 |
211,820 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 869 |
211,840 | mean_absolute_error | mean_absolute_error | parkinsons | **Author**:
**Source**: UCI
**Please cite**: 'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007)
* Abstract:
Oxford Parkinson's Disease Detection Dataset
... | {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': 147.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 48.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 195.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22.0,
'NumberOfSymbolicFeatures': 1.0,
'... | parkinsons | [
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"Class"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 870 |
211,850 | mean_absolute_error | mean_absolute_error | DEE | Daily electric energy dataset
The dee problem involves predicting the daily average price of TkWhe electricity energy in Spain. The data set contains real values from 2003 about the daily consumption in Spain of energy from hydroelectric, nuclear electric, carbon, fuel, natural gas and other special sources of energy.... | {0: [0 - Hydroelectric (numeric)],
1: [1 - Nuclear (numeric)],
2: [2 - Coal (numeric)],
3: [3 - Fuel (numeric)],
4: [4 - Gas (numeric)],
5: [5 - Special (numeric)],
6: [6 - Consume (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 365.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | DEE | [
"Hydroelectric",
"Nuclear",
"Coal",
"Fuel",
"Gas",
"Special"
] | [
false,
false,
false,
false,
false,
false
] | 871 |
211,838 | mean_absolute_error | mean_absolute_error | 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",
"class"
] | [
false,
false,
false,
false,
false,
false,
false,
true
] | 872 |
211,846 | mean_absolute_error | mean_absolute_error | treepipit | Data on the population density of tree pipits, Anthus trivialis, in Franconian oak forests including variables describing the forest ecosystem.
This data is taken from R package coin.
This study is based on fieldwork conducted in three lowland oak forests in the Franconian region of northern Bavaria close to Uffenheim,... | {0: [0 - counts (numeric)],
1: [1 - age (numeric)],
2: [2 - coverstorey (numeric)],
3: [3 - coverregen (numeric)],
4: [4 - meanregen (numeric)],
5: [5 - coniferous (numeric)],
6: [6 - deadtree (numeric)],
7: [7 - cbpiles (numeric)],
8: [8 - ivytree (numeric)],
9: [9 - fdist (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 86.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | treepipit | [
"age",
"coverstorey",
"coverregen",
"meanregen",
"coniferous",
"deadtree",
"cbpiles",
"ivytree",
"fdist"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 873 |
211,849 | mean_absolute_error | mean_absolute_error | slump | Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Concrete Slump dataset (Yeh 2007) concerns the prediction of three properties of concrete (slump, flow and compressive strength) as a function of the content of seven concrete ingredients: cement, fly ash, blast f... | {0: [0 - Cemment (numeric)],
1: [1 - Slag (numeric)],
2: [2 - Fly_ash (numeric)],
3: [3 - Water (numeric)],
4: [4 - SP (numeric)],
5: [5 - Coarse_Aggr (numeric)],
6: [6 - Fine_Aggr (numeric)],
7: [7 - SLUMP_cm (numeric)],
8: [8 - FLOW_cm (numeric)],
9: [9 - Compressive_Strength_Mpa (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 103.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | slump | [
"Cemment",
"Slag",
"Fly_ash",
"Water",
"SP",
"Coarse_Aggr",
"Fine_Aggr",
"FLOW_cm",
"Compressive_Strength_Mpa"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 874 |
211,856 | mean_absolute_error | mean_absolute_error | residential_building | **Author**: Mohammad H. Rafiei
**Source**: UCI - [original](http://archive.ics.uci.edu/ml/datasets/Residential+Building+Data+Set) - Date unknown
**Please cite**:
**Residential Building Dataset**
Dataset includes construction cost, sale prices, project variables, and economic variables corresponding to real est... | {0: [0 - Project_dates_START_YEAR (numeric)],
1: [1 - Project_dates_START_QUARTER (numeric)],
2: [2 - Project_dates_COMPLETION_YEAR (numeric)],
3: [3 - Project_dates_COMPLETION_QUARTER (numeric)],
4: [4 - Project_Physical_Financial_V.1 (numeric)],
5: [5 - Project_Physical_Financial_V.2 (numeric)],
6: [6 - Project... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 109.0,
'NumberOfInstances': 372.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 109.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | residential_building | [
"Project_dates_START_YEAR",
"Project_dates_START_QUARTER",
"Project_dates_COMPLETION_YEAR",
"Project_dates_COMPLETION_QUARTER",
"Project_Physical_Financial_V.1",
"Project_Physical_Financial_V.2",
"Project_Physical_Financial_V.3",
"Project_Physical_Financial_V.4",
"Project_Physical_Financial_V.5",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
f... | 875 |
211,858 | mean_absolute_error | mean_absolute_error | weather_ankara | **Author**:
**Source**: KEEL - [original](https://sci2s.ugr.es/keel/dataset.php?cod=41) - Date unknown
**Please cite**:
**Weather Ankara dataset**
This file contains the weather information of Ankara from 01/01/1994 to 28/05/1998. From given features, the goal is to predict the mean
temperature.
**Attribute ... | {0: [0 - Max_temperature (numeric)],
1: [1 - Min_temperature (numeric)],
2: [2 - Dewpoint (numeric)],
3: [3 - Precipitation (numeric)],
4: [4 - Sea_level_pressure (numeric)],
5: [5 - Standard_pressure (numeric)],
6: [6 - Visibility (numeric)],
7: [7 - Wind_speed (numeric)],
8: [8 - Max_wind_speed (numeric)],
9... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 321.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | weather_ankara | [
"Max_temperature",
"Min_temperature",
"Dewpoint",
"Precipitation",
"Sea_level_pressure",
"Standard_pressure",
"Visibility",
"Wind_speed",
"Max_wind_speed"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 876 |
211,844 | mean_absolute_error | mean_absolute_error | Concrete_Data | Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. | {0: [0 - Cement (component 1)(kg in a m^3 mixture) (numeric)],
1: [1 - Blast Furnace Slag (component 2)(kg in a m^3 mixture) (numeric)],
2: [2 - Fly Ash (component 3)(kg in a m^3 mixture) (numeric)],
3: [3 - Water (component 4)(kg in a m^3 mixture) (numeric)],
4: [4 - Superplasticizer (component 5)(kg in a m^3 mix... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 1030.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Concrete_Data | [
"Cement (component 1)(kg in a m^3 mixture)",
"Blast Furnace Slag (component 2)(kg in a m^3 mixture)",
"Fly Ash (component 3)(kg in a m^3 mixture)",
"Water (component 4)(kg in a m^3 mixture)",
"Superplasticizer (component 5)(kg in a m^3 mixture)",
"Coarse Aggregate (component 6)(kg in a m^3 mixture)",
... | [
false,
false,
false,
false,
false,
false,
false,
false
] | 877 |
5,099 | mean_absolute_error | mean_absolute_error | topo_2_1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This is one of 41 drug design datasets. The datasets with 1143 features are formed using Adriana.Code software (www.molecular-networks.com/software/adrianacode).
The molecules and outputs are taken from the original studies (see below). The other... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 267.0,
'NumberOfInstances': 8885.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 267.0,
'NumberOfSymbolicFeatures': 0.0,
'... | topo_2_1 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
false,
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false,
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f... | 878 |
211,854 | mean_absolute_error | mean_absolute_error | laser | **Author**:
**Source**: KEEL - [original](https://sci2s.ugr.es/keel/dataset.php?cod=47) - Date unknown
**Please cite**:
**Laser generated dataset**
This data set was originally a univariate time record of a single observed quantity, recorded from a Far-Infrared-Laser in a chaotic state. The original set 1000 ... | {0: [0 - Input1 (numeric)],
1: [1 - Input2 (numeric)],
2: [2 - Input3 (numeric)],
3: [3 - Input4 (numeric)],
4: [4 - Output (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 993.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | laser | [
"Input1",
"Input2",
"Input3",
"Input4"
] | [
false,
false,
false,
false
] | 879 |
211,852 | mean_absolute_error | mean_absolute_error | ELE-2 | Electrical-Maintenance data set
This problem consists of four input variables and the available data set is comprised of a representative number of well distributed examples. In this case, the learning methods are expected to obtain a considerable number of rules. Therefore, this problem involves a larger search space... | {0: [0 - X1 (numeric)],
1: [1 - X2 (numeric)],
2: [2 - X3 (numeric)],
3: [3 - X4 (numeric)],
4: [4 - Y (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 1056.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | ELE-2 | [
"X1",
"X2",
"X3",
"X4"
] | [
false,
false,
false,
false
] | 880 |
211,857 | mean_absolute_error | mean_absolute_error | treasury | **Author**:
**Source**: KEEL - [original](https://sci2s.ugr.es/keel/dataset.php?cod=42) - Date unknown
**Please cite**:
**Treasury Dataset**
This file contains the Economic data information of USA from 01/04/1980 to 02/04/2000 on a weekly basis. From given features, the goal is to predict 1 Month CD Rate.
**... | {0: [0 - 1Y-CMaturityRate (numeric)],
1: [1 - 30Y-CMortgageRate (numeric)],
2: [2 - 3M-Rate-AuctionAverage (numeric)],
3: [3 - 3M-Rate-SecondaryMarket (numeric)],
4: [4 - 3Y-CMaturityRate (numeric)],
5: [5 - 5Y-CMaturityRate (numeric)],
6: [6 - bankCredit (numeric)],
7: [7 - currency (numeric)],
8: [8 - demandD... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 1049.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | treasury | [
"1Y-CMaturityRate",
"30Y-CMortgageRate",
"3M-Rate-AuctionAverage",
"3M-Rate-SecondaryMarket",
"3Y-CMaturityRate",
"5Y-CMaturityRate",
"bankCredit",
"currency",
"demandDeposits",
"federalFunds",
"moneyStock",
"checkableDeposits",
"loansLeases",
"savingsDeposits",
"tradeCurrencies"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 881 |
211,860 | mean_absolute_error | mean_absolute_error | yacht_hydrodynamics | **Author**: Ship Hydromechanics Laboratory","Maritime and Transport Technology Department","Technical University of Delft.
**Source**: UCI - [original](http://archive.ics.uci.edu/ml/datasets/yacht+hydrodynamics) - Date unknown
**Please cite**:
**Yacht Hydrodynamics Dataset**
**Data Set Information**
Predictio... | {0: [0 - Logitudinal.position (numeric)],
1: [1 - Prismatic.coefficient (numeric)],
2: [2 - Length.displacement.ratio (numeric)],
3: [3 - Beam.draught.ratio (numeric)],
4: [4 - Length.beam.ratio (numeric)],
5: [5 - Froude.number (numeric)],
6: [6 - Residuary.resistance (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 308.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | yacht_hydrodynamics | [
"Logitudinal.position",
"Prismatic.coefficient",
"Length.displacement.ratio",
"Beam.draught.ratio",
"Length.beam.ratio",
"Froude.number"
] | [
false,
false,
false,
false,
false,
false
] | 882 |
211,847 | mean_absolute_error | mean_absolute_error | wine-quality-red | wine-quality-red-pmlb | {0: [0 - fixed_acidity (numeric)],
1: [1 - volatile_acidity (numeric)],
2: [2 - citric_acid (numeric)],
3: [3 - residual_sugar (numeric)],
4: [4 - chlorides (numeric)],
5: [5 - free_sulfur_dioxide (numeric)],
6: [6 - total_sulfur_dioxide (numeric)],
7: [7 - density (numeric)],
8: [8 - pH (numeric)],
9: [9 - su... | {'MajorityClassSize': 681.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 10.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1599.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0,
... | wine-quality-red | [
"fixed_acidity",
"volatile_acidity",
"citric_acid",
"residual_sugar",
"chlorides",
"free_sulfur_dioxide",
"total_sulfur_dioxide",
"density",
"pH",
"sulphates",
"class"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 883 |
211,861 | mean_absolute_error | mean_absolute_error | UCI-student-performance-mat | **Author**: P. Cortez and A. Silva
**Source**: [original](http://archive.ics.uci.edu/ml/datasets/Student+Performance) - 2008
**Please cite**: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Con... | {0: [0 - school (string)],
1: [1 - sex (string)],
2: [2 - age (numeric)],
3: [3 - address (string)],
4: [4 - famsize (string)],
5: [5 - Pstatus (string)],
6: [6 - Medu (numeric)],
7: [7 - Fedu (numeric)],
8: [8 - Mjob (string)],
9: [9 - Fjob (string)],
10: [10 - reason (string)],
11: [11 - guardian (string)]... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 395.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | UCI-student-performance-mat | [
"school",
"sex",
"age",
"address",
"famsize",
"Pstatus",
"Medu",
"Fedu",
"Mjob",
"Fjob",
"reason",
"guardian",
"traveltime",
"studytime",
"failures",
"schoolsup",
"famsup",
"paid",
"activities",
"nursery",
"higher",
"internet",
"romantic",
"famrel",
"freetime",
"goo... | [
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
] | 884 |
211,864 | mean_absolute_error | mean_absolute_error | autoMpg | Auto MPG (6 variables) dataset
The data concerns city-cycle fuel consumption in miles per gallon (Mpg), to be predicted in terms of 1 multivalued discrete and 5 continuous attributes (two multivalued discrete attributes (Cylinders and Origin) from the original dataset (autoMPG6) are removed).
This dataset is a slight... | {0: [0 - Displacement (numeric)],
1: [1 - Horse_power (numeric)],
2: [2 - Weight (numeric)],
3: [3 - Acceleration (numeric)],
4: [4 - Model_year (numeric)],
5: [5 - Mpg (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 392.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | autoMpg | [
"Displacement",
"Horse_power",
"Weight",
"Acceleration",
"Model_year"
] | [
false,
false,
false,
false,
false
] | 885 |
211,851 | mean_absolute_error | mean_absolute_error | ELE-1 | Electrical Length data set
This problem with only two input variables involves a small search space (small complexity). However, it is still an interesting problem since the system is strongly nonlinear and the available data is limited to a low number of examples presenting noise. All of these drawbacks make the mode... | {0: [0 - Inhabitants (numeric)],
1: [1 - Distance (numeric)],
2: [2 - Length (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 495.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | ELE-1 | [
"Inhabitants",
"Distance"
] | [
false,
false
] | 886 |
211,867 | predictive_accuracy | accuracy_score | autoMpg | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Identifier attribute deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
i... | {0: [0 - cylinders (nominal)],
1: [1 - displacement (numeric)],
2: [2 - horsepower (numeric)],
3: [3 - weight (numeric)],
4: [4 - acceleration (numeric)],
5: [5 - model (nominal)],
6: [6 - origin (nominal)],
7: [7 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 13.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 398.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 6.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 3.0,
'cost... | autoMpg | [
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
"model",
"origin"
] | [
true,
false,
false,
false,
false,
true,
true
] | 887 |
211,855 | mean_absolute_error | mean_absolute_error | optical_interconnection_network | ``**Author**: Cigdem Inan Aci","Mehmet Fatih Akay
**Source**: UCI - [original](http://archive.ics.uci.edu/ml/datasets/Optical+Interconnection+Network+) - Date unknown
**Please cite**:
*** Optical Interconnection Network Data Set***
### Data Set Information
All simulations have done under the software named OP... | {0: [0 - Node_Number (numeric)],
1: [1 - Thread_Number (numeric)],
2: [2 - Spatial_Distribution (nominal)],
3: [3 - Temporal_Distribution (nominal)],
4: [4 - T_R (numeric)],
5: [5 - Processor_Utilization (numeric)],
6: [6 - Channel_Waiting_Time (numeric)],
7: [7 - Input_Waiting_Time (numeric)],
8: [8 - Network_... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 640.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | optical_interconnection_network | [
"Node_Number",
"Thread_Number",
"Spatial_Distribution",
"Temporal_Distribution",
"T_R",
"Processor_Utilization",
"Channel_Waiting_Time",
"Input_Waiting_Time",
"Network_Response_Time"
] | [
false,
false,
true,
true,
false,
false,
false,
false,
false
] | 888 |
211,872 | predictive_accuracy | accuracy_score | pharynx | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Case number deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
in Connec... | {0: [0 - Inst (nominal)],
1: [1 - sex (nominal)],
2: [2 - Treatment (nominal)],
3: [3 - Grade (nominal)],
4: [4 - Age (numeric)],
5: [5 - Condition (nominal)],
6: [6 - Site (nominal)],
7: [7 - T (nominal)],
8: [8 - N (nominal)],
9: [9 - Entry (nominal)],
10: [10 - Status (nominal)],
11: [11 - class (numeric)... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 184.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 195.0,
'NumberOfInstancesWithMissingValues': 2.0,
'NumberOfMissingValues': 2.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 9.0,
'co... | pharynx | [
"Inst",
"sex",
"Treatment",
"Grade",
"Age",
"Condition",
"Site",
"T",
"N",
"Status"
] | [
true,
true,
true,
true,
false,
true,
true,
true,
true,
true
] | 889 |
211,868 | predictive_accuracy | accuracy_score | fruitfly | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Identifier attribute deleted.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
NAME: Sexual activity and the lifespan of male fruitflies
TYPE: Designed (almost factorial) experiment
SIZE: 125 observations, 5 variables
DESCRI... | {0: [0 - PARTNERS (nominal)],
1: [1 - TYPE (nominal)],
2: [2 - THORAX (numeric)],
3: [3 - SLEEP (numeric)],
4: [4 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 125.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 2.0,
'cost_... | fruitfly | [
"PARTNERS",
"TYPE",
"THORAX",
"SLEEP"
] | [
true,
true,
false,
false
] | 890 |
211,875 | predictive_accuracy | accuracy_score | pwLinear | **Author**:
**Source**: Unknown -
**Please cite**:
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
in Connectionist-Based Information Systems. Singapore: Springer-Verlag. | {0: [0 - a1 (numeric)],
1: [1 - a2 (numeric)],
2: [2 - a3 (numeric)],
3: [3 - a4 (numeric)],
4: [4 - a5 (numeric)],
5: [5 - a6 (numeric)],
6: [6 - a7 (numeric)],
7: [7 - a8 (numeric)],
8: [8 - a9 (numeric)],
9: [9 - a10 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 200.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | pwLinear | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"a7",
"a8",
"a9",
"a10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 891 |
211,873 | predictive_accuracy | accuracy_score | echoMonths | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Survival treated as the class attribute
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In P... | {0: [0 - still_alive (nominal)],
1: [1 - age (numeric)],
2: [2 - pericardial (nominal)],
3: [3 - fractional (numeric)],
4: [4 - epss (numeric)],
5: [5 - lvdd (numeric)],
6: [6 - wall_score (numeric)],
7: [7 - wall_index (numeric)],
8: [8 - alive_at_1 (nominal)],
9: [9 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 69.0,
'NumberOfMissingValues': 97.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | echoMonths | [
"still_alive",
"age",
"pericardial",
"fractional",
"epss",
"lvdd",
"wall_score",
"wall_index",
"alive_at_1"
] | [
true,
false,
true,
false,
false,
false,
false,
false,
true
] | 892 |
211,737 | predictive_accuracy | accuracy_score | satellite_image | **Author**:
**Source**: Unknown - 1993
**Please cite**:
Source:
Ashwin Srinivasan
Department of Statistics and Data Modeling
University of Strathclyde
Glasgow
Scotland
UK
ross '@' uk.ac.turing
The original Landsat data for this database was generated from data purchased from NASA by the Australian Centre for ... | {0: [0 - attr1 (numeric)],
1: [1 - attr2 (numeric)],
2: [2 - attr3 (numeric)],
3: [3 - attr4 (numeric)],
4: [4 - attr5 (numeric)],
5: [5 - attr6 (numeric)],
6: [6 - attr7 (numeric)],
7: [7 - attr8 (numeric)],
8: [8 - attr9 (numeric)],
9: [9 - attr10 (numeric)],
10: [10 - attr11 (numeric)],
11: [11 - attr12 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 6435.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 37.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | satellite_image | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6",
"attr7",
"attr8",
"attr9",
"attr10",
"attr11",
"attr12",
"attr13",
"attr14",
"attr15",
"attr16",
"attr17",
"attr18",
"attr19",
"attr20",
"attr21",
"attr22",
"attr23",
"attr24",
"attr25",
"attr26",
"attr27",
"... | [
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... | 893 |
211,808 | predictive_accuracy | accuracy_score | TurkiyeStudentEvaluation | Abstract: This data set contains a total 5820 evaluation scores provided by students from Gazi University in Ankara (Turkey). There is a total of 28 course specific questions and additional 5 attributes.
Source:
Ernest Fokoue
Center for Quality and Applied Statistics
Rochester Institute of Technology
98 Lomb Memori... | {0: [0 - instr (numeric)],
1: [1 - class (numeric)],
2: [2 - nb.repeat (numeric)],
3: [3 - attendance (numeric)],
4: [4 - difficulty (numeric)],
5: [5 - Q1 (numeric)],
6: [6 - Q2 (numeric)],
7: [7 - Q3 (numeric)],
8: [8 - Q4 (numeric)],
9: [9 - Q5 (numeric)],
10: [10 - Q6 (numeric)],
11: [11 - Q7 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 5820.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | TurkiyeStudentEvaluation | [
"instr",
"nb.repeat",
"attendance",
"difficulty",
"Q1",
"Q2",
"Q3",
"Q4",
"Q5",
"Q6",
"Q7",
"Q8",
"Q9",
"Q10",
"Q11",
"Q12",
"Q13",
"Q14",
"Q15",
"Q16",
"Q17",
"Q18",
"Q19",
"Q20",
"Q21",
"Q22",
"Q23",
"Q24",
"Q25",
"Q26",
"Q27",
"Q28"
] | [
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
] | 894 |
211,863 | mean_absolute_error | mean_absolute_error | CSM | Conventional and Social Media Movies (CSM) - Dataset 2014 and 2015 Data Set
12 features categorized as conventional and social media features. Both conventional features, collected from movies databases on Web as well as social media features(YouTube,Twitter). | {0: [0 - Movie (nominal)],
1: [1 - Year (numeric)],
2: [2 - Ratings (numeric)],
3: [3 - Genre (numeric)],
4: [4 - Gross (numeric)],
5: [5 - Budget (numeric)],
6: [6 - Screens (numeric)],
7: [7 - Sequel (numeric)],
8: [8 - Sentiment (numeric)],
9: [9 - Views (numeric)],
10: [10 - Likes (numeric)],
11: [11 - D... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 231.0,
'NumberOfInstancesWithMissingValues': 44.0,
'NumberOfMissingValues': 46.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | CSM | [
"Year",
"Ratings",
"Genre",
"Gross",
"Budget",
"Screens",
"Sequel",
"Sentiment",
"Views",
"Dislikes",
"Comments",
"Aggregate.Followers"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 895 |
211,862 | mean_absolute_error | mean_absolute_error | UCI-student-performance-por | **Author**: P. Cortez and A. Silva
**Source**: [original](http://archive.ics.uci.edu/ml/datasets/Student+Performance) - 2008
**Please cite**: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Con... | {0: [0 - school (string)],
1: [1 - sex (string)],
2: [2 - age (numeric)],
3: [3 - address (string)],
4: [4 - famsize (string)],
5: [5 - Pstatus (string)],
6: [6 - Medu (numeric)],
7: [7 - Fedu (numeric)],
8: [8 - Mjob (string)],
9: [9 - Fjob (string)],
10: [10 - reason (string)],
11: [11 - guardian (string)]... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 649.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | UCI-student-performance-por | [
"school",
"sex",
"age",
"address",
"famsize",
"Pstatus",
"Medu",
"Fedu",
"Mjob",
"Fjob",
"reason",
"guardian",
"traveltime",
"studytime",
"failures",
"schoolsup",
"famsup",
"paid",
"activities",
"nursery",
"higher",
"internet",
"romantic",
"famrel",
"freetime",
"goo... | [
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
] | 896 |
211,853 | mean_absolute_error | mean_absolute_error | forest_fires | Forest Fires Data Set
This is a difficult regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data.
Data Set Information:
In [Cortez and Morais, 2007], the output 'area' was first transformed with a ln(x+1) function.
The... | {0: [0 - X (numeric)],
1: [1 - Y (numeric)],
2: [2 - month (nominal)],
3: [3 - day (nominal)],
4: [4 - FFMC (numeric)],
5: [5 - DMC (numeric)],
6: [6 - DC (numeric)],
7: [7 - ISI (numeric)],
8: [8 - temp (numeric)],
9: [9 - RH (numeric)],
10: [10 - wind (numeric)],
11: [11 - rain (numeric)],
12: [12 - area ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 517.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | forest_fires | [
"X",
"Y",
"month",
"day",
"FFMC",
"DMC",
"DC",
"ISI",
"temp",
"RH",
"wind",
"rain"
] | [
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 897 |
211,870 | predictive_accuracy | accuracy_score | lowbwt | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Identification code deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
i... | {0: [0 - LOW (nominal)],
1: [1 - AGE (numeric)],
2: [2 - LWT (numeric)],
3: [3 - RACE (nominal)],
4: [4 - SMOKE (nominal)],
5: [5 - PTL (nominal)],
6: [6 - HT (nominal)],
7: [7 - UI (nominal)],
8: [8 - FTV (nominal)],
9: [9 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 189.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 7.0,
'cost... | lowbwt | [
"LOW",
"AGE",
"LWT",
"RACE",
"SMOKE",
"PTL",
"HT",
"UI",
"FTV"
] | [
true,
false,
false,
true,
true,
true,
true,
true,
true
] | 898 |
211,869 | predictive_accuracy | accuracy_score | pbc | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Case number deleted. X treated as the class attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length ... | {0: [0 - D (nominal)],
1: [1 - Z1 (nominal)],
2: [2 - Z2 (numeric)],
3: [3 - Z3 (nominal)],
4: [4 - Z4 (nominal)],
5: [5 - Z5 (nominal)],
6: [6 - Z6 (nominal)],
7: [7 - Z7 (nominal)],
8: [8 - Z8 (numeric)],
9: [9 - Z9 (numeric)],
10: [10 - Z10 (numeric)],
11: [11 - Z11 (numeric)],
12: [12 - Z12 (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 418.0,
'NumberOfInstancesWithMissingValues': 142.0,
'NumberOfMissingValues': 1239.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 8.0,
... | pbc | [
"D",
"Z1",
"Z2",
"Z3",
"Z4",
"Z5",
"Z6",
"Z7",
"Z8",
"Z9",
"Z10",
"Z11",
"Z12",
"Z13",
"Z14",
"Z15",
"Z16",
"Z17"
] | [
true,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 899 |
211,859 | mean_absolute_error | mean_absolute_error | weather_izmir | **Author**:
**Source**: KEEL - [original](https://sci2s.ugr.es/keel/dataset.php?cod=78) - Date unknown
**Please cite**:
**Weather Izmir dataset**
This file contains the weather information of Izmir from 01/01/1994 to 31/12/1997. From given features, the goal is to predict the mean temperature.
**Attributes I... | {0: [0 - Max_temperature (numeric)],
1: [1 - Min_temperature (numeric)],
2: [2 - Dewpoint (numeric)],
3: [3 - Precipitation (numeric)],
4: [4 - Sea_level_pressure (numeric)],
5: [5 - Standard_pressure (numeric)],
6: [6 - Visibility (numeric)],
7: [7 - Wind_speed (numeric)],
8: [8 - Max_wind_speed (numeric)],
9... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1461.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | weather_izmir | [
"Max_temperature",
"Min_temperature",
"Dewpoint",
"Precipitation",
"Sea_level_pressure",
"Standard_pressure",
"Visibility",
"Wind_speed",
"Max_wind_speed"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 900 |
211,877 | predictive_accuracy | accuracy_score | fishcatch | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Weight treated as the class attribute. Identifier deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding len... | {0: [0 - Species (nominal)],
1: [1 - Length1 (numeric)],
2: [2 - Length2 (numeric)],
3: [3 - Length3 (numeric)],
4: [4 - Height (numeric)],
5: [5 - Width (numeric)],
6: [6 - Sex (nominal)],
7: [7 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 158.0,
'NumberOfInstancesWithMissingValues': 87.0,
'NumberOfMissingValues': 87.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | fishcatch | [
"Species",
"Length1",
"Length2",
"Length3",
"Height",
"Width",
"Sex"
] | [
true,
false,
false,
false,
false,
false,
true
] | 901 |
211,814 | predictive_accuracy | accuracy_score | Students | Students | {0: [0 - instr (numeric)],
1: [1 - class (numeric)],
2: [2 - nb.repeat (numeric)],
3: [3 - attendance (numeric)],
4: [4 - difficulty (numeric)],
5: [5 - Q1 (numeric)],
6: [6 - Q2 (numeric)],
7: [7 - Q3 (numeric)],
8: [8 - Q4 (numeric)],
9: [9 - Q5 (numeric)],
10: [10 - Q6 (numeric)],
11: [11 - Q7 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 5820.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Students | [
"instr",
"nb.repeat",
"attendance",
"difficulty",
"Q1",
"Q2",
"Q3",
"Q4",
"Q5",
"Q6",
"Q7",
"Q8",
"Q9",
"Q10",
"Q11",
"Q12",
"Q13",
"Q14",
"Q15",
"Q16",
"Q17",
"Q18",
"Q19",
"Q20",
"Q21",
"Q22",
"Q23",
"Q24",
"Q25",
"Q26",
"Q27",
"Q28"
] | [
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
] | 902 |
211,876 | predictive_accuracy | accuracy_score | machine_cpu | **Author**:
**Source**: Unknown -
**Please cite**:
The problem concerns Relative CPU Performance Data. More information can be obtained in the UCI Machine
Learning repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).
The used attributes are :
MYCT: machine cycle time in nanoseconds (integer)
MMIN: ... | {0: [0 - MYCT (numeric)],
1: [1 - MMIN (numeric)],
2: [2 - MMAX (numeric)],
3: [3 - CACH (numeric)],
4: [4 - CHMIN (numeric)],
5: [5 - CHMAX (numeric)],
6: [6 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | machine_cpu | [
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
false,
false,
false,
false,
false,
false
] | 903 |
211,880 | predictive_accuracy | accuracy_score | libras_move | **Author**: Daniel Baptista Dias, Sarajane Marques Peres, Helton Hideraldo Biscaro
University of Sao Paulo, School of Art, Sciences and Humanities, Sao Paulo, SP, Brazil
**Source**: Unknown - November 2008
**Please cite**:
### LIBRAS Movement Database
LIBRAS, acronym of the Portuguese name "LIngua BRAsileira ... | {0: [0 - xcoord1 (numeric)],
1: [1 - ycoord1 (numeric)],
2: [2 - xcoord2 (numeric)],
3: [3 - ycoord2 (numeric)],
4: [4 - xcoord3 (numeric)],
5: [5 - ycoord3 (numeric)],
6: [6 - xcoord4 (numeric)],
7: [7 - ycoord4 (numeric)],
8: [8 - xcoord5 (numeric)],
9: [9 - ycoord5 (numeric)],
10: [10 - xcoord6 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 91.0,
'NumberOfInstances': 360.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 91.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | libras_move | [
"xcoord1",
"ycoord1",
"xcoord2",
"ycoord2",
"xcoord3",
"ycoord3",
"xcoord4",
"ycoord4",
"xcoord5",
"ycoord5",
"xcoord6",
"ycoord6",
"xcoord7",
"ycoord7",
"xcoord8",
"ycoord8",
"xcoord9",
"ycoord9",
"xcoord10",
"ycoord10",
"xcoord11",
"ycoord11",
"xcoord12",
"ycoord12",
... | [
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... | 904 |
211,874 | predictive_accuracy | accuracy_score | breastTumor | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Tumor-size treated as the class attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. I... | {0: [0 - age (numeric)],
1: [1 - menopause (nominal)],
2: [2 - inv-nodes (nominal)],
3: [3 - node-caps (nominal)],
4: [4 - deg-malig (nominal)],
5: [5 - breast (nominal)],
6: [6 - breast-quad (nominal)],
7: [7 - irradiation (nominal)],
8: [8 - recurrence (nominal)],
9: [9 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 18.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 286.0,
'NumberOfInstancesWithMissingValues': 9.0,
'NumberOfMissingValues': 9.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 8.0,
'cos... | breastTumor | [
"age",
"menopause",
"inv-nodes",
"node-caps",
"deg-malig",
"breast",
"breast-quad",
"irradiation",
"recurrence"
] | [
false,
true,
true,
true,
true,
true,
true,
true,
true
] | 905 |
211,899 | predictive_accuracy | accuracy_score | cpu | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Attributes 2 and 8 deleted.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Prog... | {0: [0 - vendor (nominal)],
1: [1 - MYCT (numeric)],
2: [2 - MMIN (numeric)],
3: [3 - MMAX (numeric)],
4: [4 - CACH (numeric)],
5: [5 - CHMIN (numeric)],
6: [6 - CHMAX (numeric)],
7: [7 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 30.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | cpu | [
"vendor",
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
true,
false,
false,
false,
false,
false,
false
] | 906 |
211,959 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 907 |
211,898 | predictive_accuracy | accuracy_score | bodyfat | **Author**: Roger W. Johnson
**Source**: [UCI (not available anymore)](https://archive.ics.uci.edu/ml/index.php), [TunedIT](http://tunedit.org/repo/UCI/numeric/bodyfat.arff)
**Please cite**: None.
Short Summary:
Lists estimates of the percentage of body fat determined by underwater
weighing and various body circu... | {0: [0 - Density (numeric)],
1: [1 - Age (numeric)],
2: [2 - Weight (numeric)],
3: [3 - Height (numeric)],
4: [4 - Neck (numeric)],
5: [5 - Chest (numeric)],
6: [6 - Abdomen (numeric)],
7: [7 - Hip (numeric)],
8: [8 - Thigh (numeric)],
9: [9 - Knee (numeric)],
10: [10 - Ankle (numeric)],
11: [11 - Biceps (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 252.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | bodyfat | [
"Density",
"Age",
"Weight",
"Height",
"Neck",
"Chest",
"Abdomen",
"Hip",
"Thigh",
"Knee",
"Ankle",
"Biceps",
"Forearm",
"Wrist"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 908 |
211,912 | predictive_accuracy | accuracy_score | cpu.with.vendor | null | {0: [0 - vendor (nominal)],
1: [1 - MYCT (numeric)],
2: [2 - MMIN (numeric)],
3: [3 - MMAX (numeric)],
4: [4 - CACH (numeric)],
5: [5 - CHMIN (numeric)],
6: [6 - CHMAX (numeric)],
7: [7 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 30.0,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | cpu.with.vendor | [
"vendor",
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
true,
false,
false,
false,
false,
false,
false
] | 909 |
211,901 | predictive_accuracy | accuracy_score | auto93 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Attributes 2,4, and 6 deleted. Midrange price treated as the class
attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-base... | {0: [0 - Manufacturer (nominal)],
1: [1 - Type (nominal)],
2: [2 - City_MPG (numeric)],
3: [3 - Highway_MPG (numeric)],
4: [4 - Air_Bags_standard (nominal)],
5: [5 - Drive_train_type (nominal)],
6: [6 - Number_of_cylinders (numeric)],
7: [7 - Engine_size (numeric)],
8: [8 - Horsepower (numeric)],
9: [9 - RPM (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 31.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 93.0,
'NumberOfInstancesWithMissingValues': 11.0,
'NumberOfMissingValues': 14.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 6.0,
'c... | auto93 | [
"Manufacturer",
"Type",
"City_MPG",
"Highway_MPG",
"Air_Bags_standard",
"Drive_train_type",
"Number_of_cylinders",
"Engine_size",
"Horsepower",
"RPM",
"Engine_revolutions_per_mile",
"Manual_transmission_available",
"Fuel_tank_capacity",
"Passenger_capacity",
"Length",
"Wheelbase",
"W... | [
true,
true,
false,
false,
true,
true,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 910 |
211,962 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 911 |
211,958 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 912 |
211,963 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 913 |
211,960 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 914 |
189,933 | predictive_accuracy | accuracy_score | MIP-2016-regression | source: http://plato.asu.edu/ftp/solvable.html
authors: Rolf-David Bergdoll
PAR10 performances of modern solvers on the solvable instances of MIPLIB2010.
http://miplib.zib.de/
The algorithm runtime data was directly taken from the '12 threads' table of
H. Mittelmann's evaluations.
The features were generated using t... | {0: [0 - instance_id (string)],
1: [1 - repetition (numeric)],
2: [2 - probtype (numeric)],
3: [3 - n_vars (numeric)],
4: [4 - n_constr (numeric)],
5: [5 - n_nzcnt (numeric)],
6: [6 - nq_vars (numeric)],
7: [7 - nq_constr (numeric)],
8: [8 - nq_nzcnt (numeric)],
9: [9 - lp_avg (numeric)],
10: [10 - lp_l2_avg ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 148.0,
'NumberOfInstances': 1090.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 145.0,
'NumberOfSymbolicFeatures': 2.0,
'... | MIP-2016-regression | [
"instance_id",
"repetition",
"probtype",
"n_vars",
"n_constr",
"n_nzcnt",
"nq_vars",
"nq_constr",
"nq_nzcnt",
"lp_avg",
"lp_l2_avg",
"lp_linf",
"lp_objval",
"num_b_variables",
"num_i_variables",
"num_c_variables",
"num_s_variables",
"num_n_variables",
"ratio_b_variables",
"rati... | [
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... | 915 |
211,961 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 917 |
211,997 | predictive_accuracy | accuracy_score | pbc | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Case number deleted. X treated as the class attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length ... | {0: [0 - D (nominal)],
1: [1 - Z1 (nominal)],
2: [2 - Z2 (numeric)],
3: [3 - Z3 (nominal)],
4: [4 - Z4 (nominal)],
5: [5 - Z5 (nominal)],
6: [6 - Z6 (nominal)],
7: [7 - Z7 (nominal)],
8: [8 - Z8 (numeric)],
9: [9 - Z9 (numeric)],
10: [10 - Z10 (numeric)],
11: [11 - Z11 (numeric)],
12: [12 - Z12 (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 418.0,
'NumberOfInstancesWithMissingValues': 142.0,
'NumberOfMissingValues': 1239.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 8.0,
... | pbc | [
"D",
"Z1",
"Z2",
"Z3",
"Z4",
"Z5",
"Z6",
"Z7",
"Z8",
"Z9",
"Z10",
"Z11",
"Z12",
"Z13",
"Z14",
"Z15",
"Z16",
"Z17"
] | [
true,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 918 |
211,900 | predictive_accuracy | accuracy_score | meta | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
1. Title: meta-data
2. Sources:
(a) Creator:
LIACC - University of Porto
R.Campo Alegre 823
4150 PORTO
(b) Donor: P.B.Brazdil or J.Gama Tel.: +351 600 1672
LIACC, University of Porto Fax.: +351 600 3654
Rua Campo Alegre... | {0: [0 - DS_Name (nominal)],
1: [1 - T (numeric)],
2: [2 - N (numeric)],
3: [3 - p (numeric)],
4: [4 - k (numeric)],
5: [5 - Bin (numeric)],
6: [6 - Cost (numeric)],
7: [7 - SDratio (numeric)],
8: [8 - correl (numeric)],
9: [9 - cancor1 (numeric)],
10: [10 - cancor2 (numeric)],
11: [11 - fract1 (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 24.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 528.0,
'NumberOfInstancesWithMissingValues': 264.0,
'NumberOfMissingValues': 504.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 2.0,
... | meta | [
"DS_Name",
"T",
"N",
"p",
"k",
"Bin",
"Cost",
"SDratio",
"correl",
"cancor1",
"cancor2",
"fract1",
"fract2",
"skewness",
"kurtosis",
"Hc",
"Hx",
"MCx",
"EnAtr",
"NSRatio",
"Alg_Name"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 919 |
212,001 | predictive_accuracy | accuracy_score | echoMonths | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Survival treated as the class attribute
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In P... | {0: [0 - still_alive (nominal)],
1: [1 - age (numeric)],
2: [2 - pericardial (nominal)],
3: [3 - fractional (numeric)],
4: [4 - epss (numeric)],
5: [5 - lvdd (numeric)],
6: [6 - wall_score (numeric)],
7: [7 - wall_index (numeric)],
8: [8 - alive_at_1 (nominal)],
9: [9 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 69.0,
'NumberOfMissingValues': 97.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | echoMonths | [
"still_alive",
"age",
"pericardial",
"fractional",
"epss",
"lvdd",
"wall_score",
"wall_index",
"alive_at_1"
] | [
true,
false,
true,
false,
false,
false,
false,
false,
true
] | 920 |
212,003 | predictive_accuracy | accuracy_score | pwLinear | **Author**:
**Source**: Unknown -
**Please cite**:
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. In Progress
in Connectionist-Based Information Systems. Singapore: Springer-Verlag. | {0: [0 - a1 (numeric)],
1: [1 - a2 (numeric)],
2: [2 - a3 (numeric)],
3: [3 - a4 (numeric)],
4: [4 - a5 (numeric)],
5: [5 - a6 (numeric)],
6: [6 - a7 (numeric)],
7: [7 - a8 (numeric)],
8: [8 - a9 (numeric)],
9: [9 - a10 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 200.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | pwLinear | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"a7",
"a8",
"a9",
"a10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 921 |
212,002 | predictive_accuracy | accuracy_score | breastTumor | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Tumor-size treated as the class attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
using instance-based learning with encoding length selection. I... | {0: [0 - age (numeric)],
1: [1 - menopause (nominal)],
2: [2 - inv-nodes (nominal)],
3: [3 - node-caps (nominal)],
4: [4 - deg-malig (nominal)],
5: [5 - breast (nominal)],
6: [6 - breast-quad (nominal)],
7: [7 - irradiation (nominal)],
8: [8 - recurrence (nominal)],
9: [9 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 18.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 286.0,
'NumberOfInstancesWithMissingValues': 9.0,
'NumberOfMissingValues': 9.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 8.0,
'cos... | breastTumor | [
"age",
"menopause",
"inv-nodes",
"node-caps",
"deg-malig",
"breast",
"breast-quad",
"irradiation",
"recurrence"
] | [
false,
true,
true,
true,
true,
true,
true,
true,
true
] | 923 |
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