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 |
|---|---|---|---|---|---|---|---|---|---|---|
212,000 | 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
] | 924 |
211,995 | 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
] | 925 |
212,004 | 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
] | 926 |
211,999 | 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
] | 927 |
211,996 | 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
] | 928 |
211,998 | 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
] | 929 |
211,993 | predictive_accuracy | accuracy_score | titanic_2 | titanic surviual prediction | {0: [0 - Survived (numeric)],
1: [1 - Pclass (numeric)],
2: [2 - Sex (numeric)],
3: [3 - Age (numeric)],
4: [4 - Fare (numeric)],
5: [5 - Embarked (numeric)],
6: [6 - relatives (numeric)],
7: [7 - Title (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 891.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | titanic_2 | [
"Pclass",
"Sex",
"Age",
"Fare",
"Embarked",
"relatives",
"Title"
] | [
false,
false,
false,
false,
false,
false,
false
] | 930 |
212,026 | 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
] | 931 |
212,005 | 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
] | 932 |
212,008 | 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... | 933 |
212,027 | 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
] | 934 |
212,040 | 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
] | 935 |
212,091 | 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
] | 936 |
212,029 | 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
] | 937 |
212,087 | 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
] | 938 |
211,845 | mean_absolute_error | mean_absolute_error | GeographicalOriginalofMusic | **Author**: Fang Zhou (fang.zhou '@' nottingham.edu.cn) The University of Nottinghan","Ningbo","China
**Source**: UCI
**Please cite**: Fang Zhou, Claire Q and Ross. D. King
Predicting the Geographical Origin of Music, ICDM, 2014
Abstract: Instances in this dataset contain audio features extracted from 1059 wav... | {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': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 118.0,
'NumberOfInstances': 1059.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 118.0,
'NumberOfSymbolicFeatures': 0.0,
'... | GeographicalOriginalofMusic | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 939 |
212,088 | 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
] | 940 |
233,188 | predictive_accuracy | accuracy_score | Test_vectors_1500_notrans | efe def | {0: [0 - 0 (numeric)],
1: [1 - 1 (numeric)],
2: [2 - 2 (numeric)],
3: [3 - 3 (numeric)],
4: [4 - 4 (numeric)],
5: [5 - 5 (numeric)],
6: [6 - 6 (numeric)],
7: [7 - 7 (numeric)],
8: [8 - 8 (numeric)],
9: [9 - 9 (numeric)],
10: [10 - 10 (numeric)],
11: [11 - 11 (numeric)],
12: [12 - 12 (numeric)],
13: [13 - 1... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 49.0,
'NumberOfInstances': 4.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 49.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | Test_vectors_1500_notrans | [
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"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
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"34",
"35",
"36",
"37",
"38",
"39",
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... | [
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f... | 941 |
212,086 | 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
] | 942 |
212,090 | 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
] | 944 |
211,950 | 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",
"Q9",
"Q10",
"Q11",
"Q12",
"Q13",
"Q14",
"Q15",
"Q16",
"Q17",
"Q18",
"Q19",
"Q20",
"Q21",
"Q22",
"Q23",
"Q24",
"Q25",
"Q26",
"Q27",
"Q28"
] | [
false,
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false,
false,
false,
false,
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false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 945 |
211,956 | 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
] | 946 |
359,932 | root_mean_squared_error | root_mean_squared_error | socmob | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
17x17x2x2 tables of counts in GLIM-ready format used for the analyses
in Biblarz, Timothy J., and Adrian E. Raftery. 1993. "The Effects of
Family Disruption on Social Mobility." American Sociological Review
(In press). For further details of the d... | {0: [0 - fathers_occupation (nominal)],
1: [1 - sons_occupation (nominal)],
2: [2 - family_structure (nominal)],
3: [3 - race (nominal)],
4: [4 - counts_for_sons_first_occupation (numeric)],
5: [5 - counts_for_sons_current_occupation (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 17.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 1156.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 4.0,
'cos... | socmob | [
"fathers_occupation",
"sons_occupation",
"family_structure",
"race",
"counts_for_sons_first_occupation"
] | [
true,
true,
true,
true,
false
] | 947 |
212,028 | 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"
] | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 948 |
359,934 | root_mean_squared_error | root_mean_squared_error | tecator | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This is the Tecator data set: The task is to predict the fat content of a
meat sample on the basis of its near infrared absorbance spectrum.
1. Statement of permission from Tecator (the original data source)
These data are recorded on a Tecator I... | {0: [0 - absorbance_1 (numeric)],
1: [1 - absorbance_2 (numeric)],
2: [2 - absorbance_3 (numeric)],
3: [3 - absorbance_4 (numeric)],
4: [4 - absorbance_5 (numeric)],
5: [5 - absorbance_6 (numeric)],
6: [6 - absorbance_7 (numeric)],
7: [7 - absorbance_8 (numeric)],
8: [8 - absorbance_9 (numeric)],
9: [9 - absor... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 240.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 125.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | tecator | [
"absorbance_1",
"absorbance_2",
"absorbance_3",
"absorbance_4",
"absorbance_5",
"absorbance_6",
"absorbance_7",
"absorbance_8",
"absorbance_9",
"absorbance_10",
"absorbance_11",
"absorbance_12",
"absorbance_13",
"absorbance_14",
"absorbance_15",
"absorbance_16",
"absorbance_17",
"a... | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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f... | 949 |
233,165 | precision | precision_score | Test_vectors_1500_notrans | efe def | {0: [0 - 0 (numeric)],
1: [1 - 1 (numeric)],
2: [2 - 2 (numeric)],
3: [3 - 3 (numeric)],
4: [4 - 4 (numeric)],
5: [5 - 5 (numeric)],
6: [6 - 6 (numeric)],
7: [7 - 7 (numeric)],
8: [8 - 8 (numeric)],
9: [9 - 9 (numeric)],
10: [10 - 10 (numeric)],
11: [11 - 11 (numeric)],
12: [12 - 12 (numeric)],
13: [13 - 1... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 49.0,
'NumberOfInstances': 4.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 49.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | Test_vectors_1500_notrans | [
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
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"23",
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"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
... | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 950 |
211,879 | 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",
"... | [
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false,
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false,
false,
false,
false,
f... | 951 |
233,169 | predictive_accuracy | accuracy_score | stock_fardamento02 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
valores de saida de fardamento com temperaturas, admissões e demissões | {0: [0 - qts (numeric)],
1: [1 - Material (numeric)],
2: [2 - Dia (nominal)],
3: [3 - pp (numeric)],
4: [4 - TEMP (numeric)],
5: [5 - adm (numeric)],
6: [6 - Dem (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 6277.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | stock_fardamento02 | [
"Material",
"Dia",
"pp",
"TEMP",
"adm",
"Dem"
] | [
false,
true,
false,
false,
false,
false
] | 952 |
359,933 | root_mean_squared_error | root_mean_squared_error | space_ga | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Geographical Analysis Spatial Data
This georeferenced data set was used in:
Pace, R. Kelley, and Ronald Barry, Quick Computation of Regressions with a Spatially
Autoregressive Dependent Variable, Geographical Analysis, Volume 29, Number 3, July
... | {0: [0 - ln(VOTES/POP) (numeric)],
1: [1 - POP (numeric)],
2: [2 - EDUCATION (numeric)],
3: [3 - HOUSES (numeric)],
4: [4 - INCOME (numeric)],
5: [5 - XCOORD (numeric)],
6: [6 - YCOORD (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 3107.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | space_ga | [
"POP",
"EDUCATION",
"HOUSES",
"INCOME",
"XCOORD",
"YCOORD"
] | [
false,
false,
false,
false,
false,
false
] | 953 |
233,187 | predictive_accuracy | accuracy_score | Waterstress | **Author**: Ankita Gupta, Dr.Lakwinder Kaur, Dr. Gurmeet Kaur
**Source**: Unknown - 01-11-2019
**Please cite**:
Water stress dataset for Indian variety of wheat crop:
The data consist of a file system-based data of Raj 3765 variety of wheat. There are twenty-four chlorophyll fluorescence images captured ever... | {0: [0 - autoc (numeric)],
1: [1 - contr (numeric)],
2: [2 - corrm (numeric)],
3: [3 - corrp (numeric)],
4: [4 - cprom (numeric)],
5: [5 - cshad (numeric)],
6: [6 - dissi (numeric)],
7: [7 - energ (numeric)],
8: [8 - entro (numeric)],
9: [9 - homom1 (numeric)],
10: [10 - homop (numeric)],
11: [11 - maxpr (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 1188.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 23.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Waterstress | [
"autoc",
"contr",
"corrm",
"corrp",
"cprom",
"cshad",
"dissi",
"energ",
"entro",
"homom1",
"homop",
"maxpr",
"sosvh",
"savgh",
"svarh",
"senth",
"dvarh",
"denth",
"inf1h",
"inf2h",
"homom",
"indnc"
] | [
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 954 |
212,078 | 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"
] | [
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false,
false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 955 |
360,129 | predictive_accuracy | accuracy_score | Test_vectors_1500_notrans | efe def | {0: [0 - 0 (numeric)],
1: [1 - 1 (numeric)],
2: [2 - 2 (numeric)],
3: [3 - 3 (numeric)],
4: [4 - 4 (numeric)],
5: [5 - 5 (numeric)],
6: [6 - 6 (numeric)],
7: [7 - 7 (numeric)],
8: [8 - 8 (numeric)],
9: [9 - 9 (numeric)],
10: [10 - 10 (numeric)],
11: [11 - 11 (numeric)],
12: [12 - 12 (numeric)],
13: [13 - 1... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 49.0,
'NumberOfInstances': 4.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 49.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | Test_vectors_1500_notrans | [
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
... | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 956 |
360,956 | predictive_accuracy | accuracy_score | Test_vectors_1500_notrans | efe def | {0: [0 - 0 (numeric)],
1: [1 - 1 (numeric)],
2: [2 - 2 (numeric)],
3: [3 - 3 (numeric)],
4: [4 - 4 (numeric)],
5: [5 - 5 (numeric)],
6: [6 - 6 (numeric)],
7: [7 - 7 (numeric)],
8: [8 - 8 (numeric)],
9: [9 - 9 (numeric)],
10: [10 - 10 (numeric)],
11: [11 - 11 (numeric)],
12: [12 - 12 (numeric)],
13: [13 - 1... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 49.0,
'NumberOfInstances': 4.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 49.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | Test_vectors_1500_notrans | [
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
... | [
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 957 |
361,014 | root_mean_squared_error | root_mean_squared_error | AAPL_stock_price_2021_2022_1 | Apple stock price of each trading day since January 1st 2021. Contains highest price, lowest price, open, close, volume and adjusted close | {0: [0 - Date (string)],
1: [1 - Open (numeric)],
2: [2 - High (numeric)],
3: [3 - Low (numeric)],
4: [4 - Close (numeric)],
5: [5 - Adj Close (numeric)],
6: [6 - Volume (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 347.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | AAPL_stock_price_2021_2022_1 | [
"Open",
"High",
"Low",
"Adj Close",
"Volume"
] | [
false,
false,
false,
false,
false
] | 958 |
359,935 | root_mean_squared_error | root_mean_squared_error | wine_quality | **Author**: Tobias Kuehn
**Source**: Unknown - 2009
**Please cite**:
1. Title: Wine Quality
2. Sources
Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009
3. Past Usage:
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
Modeling wi... | {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': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 6497.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | wine_quality | [
"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
] | 959 |
360,992 | 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
] | 960 |
212,007 | 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,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 961 |
212,084 | 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
] | 962 |
361,002 | predictive_accuracy | accuracy_score | Bank-Note-Authentication-UCI | Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution ... | {0: [0 - variance (numeric)],
1: [1 - skewness (numeric)],
2: [2 - curtosis (numeric)],
3: [3 - entropy (numeric)],
4: [4 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 1372.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Bank-Note-Authentication-UCI | [
"variance",
"skewness",
"curtosis",
"entropy"
] | [
false,
false,
false,
false
] | 963 |
360,993 | precision | precision_score | 18ProductivityPrediction | 18ProductivityPrediction | {0: [0 - date (nominal)],
1: [1 - quarter (nominal)],
2: [2 - department (nominal)],
3: [3 - day (nominal)],
4: [4 - team (numeric)],
5: [5 - targeted_productivity (numeric)],
6: [6 - smv (numeric)],
7: [7 - wip (numeric)],
8: [8 - over_time (numeric)],
9: [9 - incentive (numeric)],
10: [10 - idle_time (numer... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 1197.0,
'NumberOfInstancesWithMissingValues': 506.0,
'NumberOfMissingValues': 506.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 4.0,
... | 18ProductivityPrediction | [
"date",
"quarter",
"department",
"day",
"team",
"targeted_productivity",
"smv",
"wip",
"over_time",
"incentive",
"idle_time",
"idle_men",
"no_of_style_change",
"no_of_workers"
] | [
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 964 |
361,013 | root_mean_squared_error | root_mean_squared_error | AAPL_stock_price_2021_2022 | Apple stock price of each work day since January 1st 2021. Contains highest price, lowest price, open, close, volume and adjusted close | {0: [0 - Date (string)],
1: [1 - High (numeric)],
2: [2 - Low (numeric)],
3: [3 - Open (numeric)],
4: [4 - Close (numeric)],
5: [5 - Volume (numeric)],
6: [6 - Adj Close (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 346.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | AAPL_stock_price_2021_2022 | [
"High",
"Low",
"Open",
"Volume",
"Adj Close"
] | [
false,
false,
false,
false,
false
] | 965 |
360,998 | root_mean_squared_error | root_mean_squared_error | wine_quality | wine-quality data for testing in automl-benchmark | {0: [0 - fixedacidity (numeric)],
1: [1 - volatileacidity (numeric)],
2: [2 - citricacid (numeric)],
3: [3 - residualsugar (numeric)],
4: [4 - chlorides (numeric)],
5: [5 - freesulfurdioxide (numeric)],
6: [6 - totalsulfurdioxide (numeric)],
7: [7 - density (numeric)],
8: [8 - ph (numeric)],
9: [9 - sulphates ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 1143.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | wine_quality | [
"fixedacidity",
"volatileacidity",
"citricacid",
"residualsugar",
"chlorides",
"freesulfurdioxide",
"totalsulfurdioxide",
"density",
"ph",
"sulphates",
"alcohol",
"id"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 966 |
361,003 | predictive_accuracy | accuracy_score | Wisconsin-breast-cancer-cytology-features | Context
Cytology features of breast cancer biopsy. It can be used to predict breast cancer from cytology features.
The data was obtained from https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original)
Data description can be found at https://archive.ics.uci.edu/ml/machine-learning-databases/breast-canc... | {0: [0 - id (numeric)],
1: [1 - thickness (numeric)],
2: [2 - size (numeric)],
3: [3 - shape (numeric)],
4: [4 - adhesion (numeric)],
5: [5 - single (numeric)],
6: [6 - nuclei (numeric)],
7: [7 - chromatin (numeric)],
8: [8 - nucleoli (numeric)],
9: [9 - mitosis (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 699.0,
'NumberOfInstancesWithMissingValues': 16.0,
'NumberOfMissingValues': 16.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | Wisconsin-breast-cancer-cytology-features | [
"id",
"thickness",
"size",
"shape",
"adhesion",
"single",
"nuclei",
"chromatin",
"nucleoli",
"mitosis"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 967 |
360,128 | predictive_accuracy | accuracy_score | Waterstress | **Author**: Ankita Gupta, Dr.Lakwinder Kaur, Dr. Gurmeet Kaur
**Source**: Unknown - 01-11-2019
**Please cite**:
Water stress dataset for Indian variety of wheat crop:
The data consist of a file system-based data of Raj 3765 variety of wheat. There are twenty-four chlorophyll fluorescence images captured ever... | {0: [0 - autoc (numeric)],
1: [1 - contr (numeric)],
2: [2 - corrm (numeric)],
3: [3 - corrp (numeric)],
4: [4 - cprom (numeric)],
5: [5 - cshad (numeric)],
6: [6 - dissi (numeric)],
7: [7 - energ (numeric)],
8: [8 - entro (numeric)],
9: [9 - homom1 (numeric)],
10: [10 - homop (numeric)],
11: [11 - maxpr (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 1188.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 23.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Waterstress | [
"autoc",
"contr",
"corrm",
"corrp",
"cprom",
"cshad",
"dissi",
"energ",
"entro",
"homom1",
"homop",
"maxpr",
"sosvh",
"savgh",
"svarh",
"senth",
"dvarh",
"denth",
"inf1h",
"inf2h",
"homom",
"indnc"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 968 |
361,016 | root_mean_squared_error | root_mean_squared_error | AAPL_stock_price_2021_2022_2 | Apple stock price of each work day since January 1st 2021. Contains highest price, lowest price, open, close, volume and adjusted close | {0: [0 - High (numeric)],
1: [1 - Low (numeric)],
2: [2 - Open (numeric)],
3: [3 - Close (numeric)],
4: [4 - Volume (numeric)],
5: [5 - Adj Close (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 348.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | AAPL_stock_price_2021_2022_2 | [
"High",
"Low",
"Open",
"Volume",
"Adj Close"
] | [
false,
false,
false,
false,
false
] | 969 |
361,076 | root_mean_squared_error | root_mean_squared_error | wine_quality | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
**Author**: Tobias Kuehn
**Source**: Unknown - 2009
**Please cite**:
1. Title: Wine Quality
2... | {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': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 6497.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | wine_quality | [
"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
] | 970 |
360,955 | predictive_accuracy | accuracy_score | Waterstress | **Author**: Ankita Gupta, Dr.Lakwinder Kaur, Dr. Gurmeet Kaur
**Source**: Unknown - 01-11-2019
**Please cite**:
Water stress dataset for Indian variety of wheat crop:
The data consist of a file system-based data of Raj 3765 variety of wheat. There are twenty-four chlorophyll fluorescence images captured ever... | {0: [0 - autoc (numeric)],
1: [1 - contr (numeric)],
2: [2 - corrm (numeric)],
3: [3 - corrp (numeric)],
4: [4 - cprom (numeric)],
5: [5 - cshad (numeric)],
6: [6 - dissi (numeric)],
7: [7 - energ (numeric)],
8: [8 - entro (numeric)],
9: [9 - homom1 (numeric)],
10: [10 - homop (numeric)],
11: [11 - maxpr (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 1188.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 23.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Waterstress | [
"autoc",
"contr",
"corrm",
"corrp",
"cprom",
"cshad",
"dissi",
"energ",
"entro",
"homom1",
"homop",
"maxpr",
"sosvh",
"savgh",
"svarh",
"senth",
"dvarh",
"denth",
"inf1h",
"inf2h",
"homom",
"indnc"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 971 |
361,093 | root_mean_squared_error | root_mean_squared_error | analcatdata_supreme | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "regression on categorical and
numerical features" benchmark. Original description:
**Author**:
**... | {0: [0 - Actions_taken (numeric)],
1: [1 - Liberal (nominal)],
2: [2 - Unconstitutional (nominal)],
3: [3 - Precedent_alteration (nominal)],
4: [4 - Unanimous (nominal)],
5: [5 - Year_of_decision (numeric)],
6: [6 - Lower_court_disagreement (nominal)],
7: [7 - Log_exposure (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 4052.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
'cost... | analcatdata_supreme | [
"Actions_taken",
"Liberal",
"Unconstitutional",
"Precedent_alteration",
"Unanimous",
"Year_of_decision",
"Lower_court_disagreement"
] | [
false,
true,
true,
true,
true,
false,
true
] | 972 |
361,132 | root_mean_squared_error | root_mean_squared_error | Reading_Hydro_upstream | Upstream data from the twin Archimedes screw hydro-electric generator on the river Thames at Caversham weir, Reading, UK. | {0: [0 - timestamp (numeric)], 1: [1 - upstream (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Reading_Hydro_upstream | [
"timestamp"
] | [
false
] | 974 |
361,094 | root_mean_squared_error | root_mean_squared_error | visualizing_soil | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "regression on categorical and
numerical features" benchmark. Original description:
**Author**:
**... | {0: [0 - northing (numeric)],
1: [1 - easting (numeric)],
2: [2 - resistivity (numeric)],
3: [3 - isns (nominal)],
4: [4 - track (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 8641.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | visualizing_soil | [
"northing",
"easting",
"resistivity",
"isns"
] | [
false,
false,
false,
true
] | 975 |
361,138 | root_mean_squared_error | root_mean_squared_error | Reading_Hydro_upstream | Upstream data from the twin Archimedes screw hydro-electric generator on the river Thames at Caversham weir, Reading, UK. | {0: [0 - timestamp (numeric)], 1: [1 - downstream (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Reading_Hydro_upstream | [
"timestamp"
] | [
false
] | 976 |
361,280 | root_mean_squared_error | root_mean_squared_error | abalone | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark.
Original link: https://openml.org/d/42726
Original description:
Make target (age) numeric**Author**:
**Source**: ... | {0: [0 - Length (numeric)],
1: [1 - Diameter (numeric)],
2: [2 - Height (numeric)],
3: [3 - Whole_weight (numeric)],
4: [4 - Shucked_weight (numeric)],
5: [5 - Viscera_weight (numeric)],
6: [6 - Shell_weight (numeric)],
7: [7 - Classnumberofrings (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 4177.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | abalone | [
"Length",
"Diameter",
"Height",
"Whole_weight",
"Shucked_weight",
"Viscera_weight",
"Shell_weight"
] | [
false,
false,
false,
false,
false,
false,
false
] | 977 |
362,072 | root_mean_squared_error | root_mean_squared_error | NCI_60_Thioguanine | This pharmacogenomic study investigates the patterns of drug activity in cancer cell lines. These cell lines come from the NCI-60 Human Tumor Cell Lines established by the Developmental Therapeutics Program of the National Cancer Institute (NCI) to screen for the toxicity of chemical compound repositories in diverse ca... | {0: [0 - ABCA1 (numeric)],
1: [1 - ABCA2 (numeric)],
2: [2 - ABCA3 (numeric)],
3: [3 - ABCA4 (numeric)],
4: [4 - ABCA5 (numeric)],
5: [5 - ABCA6 (numeric)],
6: [6 - ABCA7 (numeric)],
7: [7 - ABCA8 (numeric)],
8: [8 - ABCA9 (numeric)],
9: [9 - ABCA10 (numeric)],
10: [10 - ABCA12 (numeric)],
11: [11 - ABCA13 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 49.0,
'NumberOfInstances': 60.0,
'NumberOfInstancesWithMissingValues': 1.0,
'NumberOfMissingValues': 1.0,
'NumberOfNumericFeatures': 49.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | NCI_60_Thioguanine | [
"ABCA1",
"ABCA2",
"ABCA3",
"ABCA4",
"ABCA5",
"ABCA6",
"ABCA7",
"ABCA8",
"ABCA9",
"ABCA10",
"ABCA12",
"ABCA13",
"ABCB1",
"ABCB2",
"ABCB3",
"ABCB4",
"ABCB5",
"ABCB6",
"ABCB7",
"ABCB8",
"ABCB9",
"ABCB10",
"ABCB11",
"ABCC1",
"ABCC2",
"ABCC3",
"ABCC4",
"ABCC5",
"AB... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 979 |
361,288 | root_mean_squared_error | root_mean_squared_error | abalone | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical features" benchmark.
Original link: https://openml.org/d/42726
Original description:
Make target (age) numeric**Auth... | {0: [0 - Sex (nominal)],
1: [1 - Length (numeric)],
2: [2 - Diameter (numeric)],
3: [3 - Height (numeric)],
4: [4 - Whole_weight (numeric)],
5: [5 - Shucked_weight (numeric)],
6: [6 - Viscera_weight (numeric)],
7: [7 - Shell_weight (numeric)],
8: [8 - Classnumberofrings (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 4177.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | abalone | [
"Sex",
"Length",
"Diameter",
"Height",
"Whole_weight",
"Shucked_weight",
"Viscera_weight",
"Shell_weight"
] | [
true,
false,
false,
false,
false,
false,
false,
false
] | 980 |
361,072 | root_mean_squared_error | root_mean_squared_error | cpu_act | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown -
**Please cite**:
The Computer Activity databases are a coll... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - pgout (numeric)],
10: [10 - ppgout (numeric)],
11: [11 - pgfree (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | cpu_act | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"pgout",
"ppgout",
"pgfree",
"pgscan",
"atch",
"pgin",
"ppgin",
"pflt",
"vflt",
"runqsz",
"freemem",
"freeswap"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 981 |
361,033 | root_mean_squared_error | root_mean_squared_error | cpu_act | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown -
**Please cite**:
The Computer Activity databases are a coll... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - pgout (numeric)],
10: [10 - ppgout (numeric)],
11: [11 - pgfree (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | cpu_act | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"pgout",
"ppgout",
"pgfree",
"pgscan",
"atch",
"pgin",
"ppgin",
"pflt",
"vflt",
"runqsz",
"freemem",
"freeswap"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 982 |
359,951 | root_mean_squared_error | root_mean_squared_error | house_prices_nominal | **Author**: Kaggle
**Source**: [original](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) - 2011
**Please cite**: Dean De Cock (2011) Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project, Journal of Statistics Education, 19:3, DOI: 10.1080/10691898.2011.1... | {0: [0 - Id (numeric)],
1: [1 - MSSubClass (numeric)],
2: [2 - MSZoning (nominal)],
3: [3 - LotFrontage (numeric)],
4: [4 - LotArea (numeric)],
5: [5 - Street (nominal)],
6: [6 - Alley (nominal)],
7: [7 - LotShape (nominal)],
8: [8 - LandContour (nominal)],
9: [9 - Utilities (nominal)],
10: [10 - LotConfig (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 80.0,
'NumberOfInstances': 1460.0,
'NumberOfInstancesWithMissingValues': 1460.0,
'NumberOfMissingValues': 6965.0,
'NumberOfNumericFeatures': 37.0,
'NumberOfSymbolicFeatures': 43.... | house_prices_nominal | [
"MSSubClass",
"MSZoning",
"LotFrontage",
"LotArea",
"Street",
"Alley",
"LotShape",
"LandContour",
"Utilities",
"LotConfig",
"LandSlope",
"Neighborhood",
"Condition1",
"Condition2",
"BldgType",
"HouseStyle",
"OverallQual",
"OverallCond",
"YearBuilt",
"YearRemodAdd",
"RoofStyle... | [
false,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
false,
true,
false,
false,
false,
... | 983 |
362,337 | mean_absolute_error | mean_absolute_error | mytestdataset | une description
test | {0: [0 - age (numeric)],
1: [1 - weight (numeric)],
2: [2 - Height (numeric)],
3: [3 - Sex (string)],
4: [4 - ID (numeric)],
5: [5 - ID_test (string)],
6: [6 - AgoraPhobia (numeric)],
7: [7 - Claustrophobia (numeric)],
8: [8 - Acrophobia (numeric)],
9: [9 - Pteromerhanophobia (numeric)],
10: [10 - Entomophobi... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 992.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | mytestdataset | [
"age",
"weight",
"Height",
"Sex",
"ID",
"ID_test",
"AgoraPhobia",
"Claustrophobia",
"Acrophobia",
"Pteromerhanophobia",
"Entomophobia",
"Ophidiophobia",
"Cynophobia",
"Astraphobia",
"Trypanophobia"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 984 |
362,348 | mean_absolute_error | mean_absolute_error | Fish-market | Content
This dataset is a record of 7 common different fish species in fish market sales. With this dataset, a predictive model can be performed using machine friendly data and estimate the weight of fish can be predicted.
Acknowledgements
Thanks to all who make Kernels using this dataset and also people viewed or dow... | {0: [0 - Species (string)],
1: [1 - Weight (numeric)],
2: [2 - Length1 (numeric)],
3: [3 - Length2 (numeric)],
4: [4 - Length3 (numeric)],
5: [5 - Height (numeric)],
6: [6 - Width (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | Fish-market | [
"Species",
"Length1",
"Length2",
"Length3",
"Height",
"Width"
] | [
false,
false,
false,
false,
false,
false
] | 985 |
362,357 | mean_absolute_error | mean_absolute_error | Diabetes-Data-Set | Context
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes.
Content
Several constraints were placed on the selection of these instances from a larger database. In particular, al... | {0: [0 - Pregnancies (numeric)],
1: [1 - Glucose (numeric)],
2: [2 - BloodPressure (numeric)],
3: [3 - SkinThickness (numeric)],
4: [4 - Insulin (numeric)],
5: [5 - BMI (numeric)],
6: [6 - DiabetesPedigreeFunction (numeric)],
7: [7 - Age (numeric)],
8: [8 - Outcome (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 768.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | Diabetes-Data-Set | [
"Pregnancies",
"Glucose",
"BloodPressure",
"SkinThickness",
"Insulin",
"BMI",
"DiabetesPedigreeFunction",
"Age"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 986 |
362,364 | mean_absolute_error | mean_absolute_error | The-Office-Dataset | Context
The Office is an American Mockumentary sitcom television series that depicts the everyday lives of office employees in the Scranton, Pennsylvania, branch of the fictional Dunder Mifflin Paper Company.
Content
The dataset consists of 12 columns and 188 rows scrapped from IMDb.
Acknowledgements
IMDb : https://ww... | {0: [0 - Unnamed:_0 (numeric)],
1: [1 - Season (numeric)],
2: [2 - EpisodeTitle (string)],
3: [3 - About (string)],
4: [4 - Ratings (numeric)],
5: [5 - Votes (numeric)],
6: [6 - Viewership (numeric)],
7: [7 - Duration (numeric)],
8: [8 - Date (string)],
9: [9 - GuestStars (string)],
10: [10 - Director (string... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 188.0,
'NumberOfInstancesWithMissingValues': 159.0,
'NumberOfMissingValues': 159.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'... | The-Office-Dataset | [
"Unnamed:_0",
"Season",
"EpisodeTitle",
"About",
"Votes",
"Viewership",
"Duration",
"Date",
"GuestStars",
"Director",
"Writers"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 987 |
362,363 | mean_absolute_error | mean_absolute_error | Forest-Fire-Area | Content
The dataset contains 517 fires from the Montesinho natural park in Portugal. For each incident weekday, month, coordinates, and the burnt area are recorded, as well as several meteorological data such as rain, temperature, humidity, and wind. The workflow reads the data and trains a regression model based on th... | {0: [0 - X (numeric)],
1: [1 - Y (numeric)],
2: [2 - month (string)],
3: [3 - day (string)],
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 (n... | {'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': 0.0,
'cos... | Forest-Fire-Area | [
"X",
"Y",
"month",
"day",
"FFMC",
"DMC",
"DC",
"ISI",
"temp",
"RH",
"wind",
"rain"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 988 |
362,371 | mean_absolute_error | mean_absolute_error | Swiss-banknote-conterfeit-detection | Context
Will you be able to identify genuine and conterfeit banknotes, even if half of the data is conterfeit? Perfect for testing different outlier detection algorithms.
Content
The dataset includes information about the shape of the bill, as well as the label. It is made up of 200 banknotes in total, 100 for genuine/... | {0: [0 - conterfeit (numeric)],
1: [1 - Length (numeric)],
2: [2 - Left (numeric)],
3: [3 - Right (numeric)],
4: [4 - Bottom (numeric)],
5: [5 - Top (numeric)],
6: [6 - Diagonal (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 200.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | Swiss-banknote-conterfeit-detection | [
"Length",
"Left",
"Right",
"Bottom",
"Top",
"Diagonal"
] | [
false,
false,
false,
false,
false,
false
] | 990 |
362,359 | mean_absolute_error | mean_absolute_error | Indian-Liver-Patient-Patient-Records-KFolds-5folds | Context
Liver disease are in India are increasing due to excessive consumption of alcohol and other harmful substaces present in air or food items or drugs. This dataset is created for predictive analysis of the liver disease to reduce the burden on the doctors.
Content
This data set contains 416 liver patient records ... | {0: [0 - Age (numeric)],
1: [1 - Gender (numeric)],
2: [2 - Total_Bilirubin (numeric)],
3: [3 - Direct_Bilirubin (numeric)],
4: [4 - Alkaline_Phosphotase (numeric)],
5: [5 - Alamine_Aminotransferase (numeric)],
6: [6 - Aspartate_Aminotransferase (numeric)],
7: [7 - Total_Protiens (numeric)],
8: [8 - Albumin (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 583.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Indian-Liver-Patient-Patient-Records-KFolds-5folds | [
"Age",
"Gender",
"Total_Bilirubin",
"Direct_Bilirubin",
"Alkaline_Phosphotase",
"Alamine_Aminotransferase",
"Aspartate_Aminotransferase",
"Total_Protiens",
"Albumin",
"Albumin_and_Globulin_Ratio",
"kfold"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 991 |
362,366 | mean_absolute_error | mean_absolute_error | student-grade-pass-or-fail-prediction | This dataset is based on dipam7/student-grade-prediction
Dataset
General information
This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school-related features) and it was collected by using school reports and ques... | {0: [0 - school (numeric)],
1: [1 - sex (numeric)],
2: [2 - age (numeric)],
3: [3 - address (numeric)],
4: [4 - famsize (numeric)],
5: [5 - Pstatus (numeric)],
6: [6 - Medu (numeric)],
7: [7 - Fedu (numeric)],
8: [8 - traveltime (numeric)],
9: [9 - studytime (numeric)],
10: [10 - failures (numeric)],
11: [11... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 395.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 30.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | student-grade-pass-or-fail-prediction | [
"school",
"sex",
"age",
"address",
"famsize",
"Pstatus",
"Medu",
"Fedu",
"traveltime",
"studytime",
"failures",
"schoolsup",
"famsup",
"paid",
"activities",
"nursery",
"higher",
"internet",
"romantic",
"famrel",
"freetime",
"goout",
"Dalc",
"Walc",
"health",
"absenc... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 992 |
362,358 | mean_absolute_error | mean_absolute_error | The-Estonia-Disaster-Passenger-List | Introduction
On September 27 1994 the ferry Estonia set sail on a night voyage across the Baltic Sea from the port of Tallin in Estonia to Stockholm. She departed at 19.00 carrying 989 passengers and crew, as well as vehicles, and was due to dock at 09.30 the following morning, Tragically, the Estonia never arrived.
Th... | {0: [0 - PassengerId (numeric)],
1: [1 - Country (string)],
2: [2 - Firstname (string)],
3: [3 - Lastname (string)],
4: [4 - Sex (string)],
5: [5 - Age (numeric)],
6: [6 - Category (string)],
7: [7 - Survived (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 989.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | The-Estonia-Disaster-Passenger-List | [
"Country",
"Firstname",
"Lastname",
"Sex",
"Age",
"Category"
] | [
false,
false,
false,
false,
false,
false
] | 993 |
3,009 | predictive_accuracy | accuracy_score | isolet | **Author**: Ron Cole and Mark Fanty (cole@cse.ogi.edu, fanty@cse.ogi.edu)
**Donor**: Tom Dietterich (tgd@cs.orst.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/ISOLET)
**Please cite**: UCI
### Description
ISOLET (Isolated Letter Speech Recognition) dataset was generated as follows: 150 subjec... | {0: [0 - f1 (numeric)],
1: [1 - f2 (numeric)],
2: [2 - f3 (numeric)],
3: [3 - f4 (numeric)],
4: [4 - f5 (numeric)],
5: [5 - f6 (numeric)],
6: [6 - f7 (numeric)],
7: [7 - f8 (numeric)],
8: [8 - f9 (numeric)],
9: [9 - f10 (numeric)],
10: [10 - f11 (numeric)],
11: [11 - f12 (numeric)],
12: [12 - f13 (numeric)]... | {'MajorityClassSize': 300.0,
'MaxNominalAttDistinctValues': 26.0,
'MinorityClassSize': 298.0,
'NumberOfClasses': 26.0,
'NumberOfFeatures': 618.0,
'NumberOfInstances': 7797.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 617.0,
'NumberOfSymbolicFeatures': 1... | isolet | [
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"f7",
"f8",
"f9",
"f10",
"f11",
"f12",
"f13",
"f14",
"f15",
"f16",
"f17",
"f18",
"f19",
"f20",
"f21",
"f22",
"f23",
"f24",
"f25",
"f26",
"f27",
"f28",
"f29",
"f30",
"f31",
"f32",
"f33",
"f34",
"f35",
"f36",
"... | [
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f... | 994 |
362,308 | mean_absolute_error | mean_absolute_error | shill-bidding | We scraped a large number of eBay auctions of a popular product. After preprocessing the auction data, we build the SB dataset. The goal is to share the labelled SB dataset with the researchers. | {0: [0 - Record_ID (numeric)],
1: [1 - Auction_ID (numeric)],
2: [2 - Bidder_ID (string)],
3: [3 - Bidder_Tendency (numeric)],
4: [4 - Bidding_Ratio (numeric)],
5: [5 - Successive_Outbidding (numeric)],
6: [6 - Last_Bidding (numeric)],
7: [7 - Auction_Bids (numeric)],
8: [8 - Starting_Price_Average (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 6321.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | shill-bidding | [
"Record_ID",
"Auction_ID",
"Bidder_ID",
"Bidder_Tendency",
"Bidding_Ratio",
"Successive_Outbidding",
"Last_Bidding",
"Auction_Bids",
"Starting_Price_Average",
"Early_Bidding",
"Winning_Ratio",
"Auction_Duration"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 995 |
362,356 | mean_absolute_error | mean_absolute_error | Pokmon-Legendary-Data | Context
In the world of Pokmon academia, one name towers above any other Professor Samuel Oak. While his colleague Professor Elm specializes in Pokmon evolution, Oak has dedicated his career to understanding the relationship between Pokmon and their human trainers. A former trainer himself, the professor has first-han... | {0: [0 - pokedex_number (numeric)],
1: [1 - name (string)],
2: [2 - attack (numeric)],
3: [3 - defense (numeric)],
4: [4 - height_m (numeric)],
5: [5 - hp (numeric)],
6: [6 - percentage_male (numeric)],
7: [7 - sp_attack (numeric)],
8: [8 - sp_defense (numeric)],
9: [9 - speed (numeric)],
10: [10 - type (stri... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 801.0,
'NumberOfInstancesWithMissingValues': 117.0,
'NumberOfMissingValues': 138.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
... | Pokmon-Legendary-Data | [
"pokedex_number",
"name",
"attack",
"defense",
"height_m",
"hp",
"percentage_male",
"sp_attack",
"sp_defense",
"speed",
"type",
"weight_kg",
"generation"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 996 |
362,381 | mean_absolute_error | mean_absolute_error | Lisbon-House-Prices | Context
Explore the regression algorithm using the prices of Lisbon's houses. This dataset contains a total of 246 records.
Content
The attributes of this dataset are:
Id: is a unique identifying number assigned to each house.
Condition: The house condition (i.e., New, Used, As New, For Refurbishment).
PropertyType:... | {0: [0 - Id (numeric)],
1: [1 - Condition (string)],
2: [2 - PropertyType (string)],
3: [3 - PropertySubType (string)],
4: [4 - Bedrooms (numeric)],
5: [5 - Bathrooms (numeric)],
6: [6 - AreaNet (numeric)],
7: [7 - AreaGross (numeric)],
8: [8 - Parking (numeric)],
9: [9 - Latitude (numeric)],
10: [10 - Longit... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 246.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Lisbon-House-Prices | [
"Id",
"Condition",
"PropertyType",
"PropertySubType",
"Bedrooms",
"Bathrooms",
"AreaNet",
"AreaGross",
"Parking",
"Latitude",
"Longitude",
"Country",
"District",
"Municipality",
"Parish",
"Price_M2"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 997 |
362,075 | root_mean_squared_error | root_mean_squared_error | Cancer_Drug_Response_expression | The dataset is obtained from Qiao Liu et al. (3). | {0: [0 - LASP1 (numeric)],
1: [1 - HOXA11 (numeric)],
2: [2 - CREBBP (numeric)],
3: [3 - ETV1 (numeric)],
4: [4 - GAS7 (numeric)],
5: [5 - CD79B (numeric)],
6: [6 - PAX7 (numeric)],
7: [7 - BTK (numeric)],
8: [8 - BRCA1 (numeric)],
9: [9 - WAS (numeric)],
10: [10 - WWTR1 (numeric)],
11: [11 - CD74 (numeric)]... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 698.0,
'NumberOfInstances': 475.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 698.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | Cancer_Drug_Response_expression | [
"LASP1",
"HOXA11",
"CREBBP",
"ETV1",
"GAS7",
"CD79B",
"PAX7",
"BTK",
"BRCA1",
"WAS",
"WWTR1",
"CD74",
"BIRC3",
"FAS",
"BCLAF1",
"ANK1",
"RABEP1",
"ZCCHC8",
"CUL3",
"FLT4",
"CDH1",
"CDH10",
"TNC",
"EPHA3",
"PREX2",
"TPR",
"GOPC",
"ROS1",
"TNFRSF17",
"ELN",
... | [
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false,
false,
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f... | 998 |
362,365 | mean_absolute_error | mean_absolute_error | Is-this-a-good-customer | Context
Imbalanced classes put accuracy out of business. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class.
Content
Standard accuracy no longer reliably measures performance, which makes model tra... | {0: [0 - month (numeric)],
1: [1 - credit_amount (numeric)],
2: [2 - credit_term (numeric)],
3: [3 - age (numeric)],
4: [4 - sex (string)],
5: [5 - education (string)],
6: [6 - product_type (string)],
7: [7 - having_children_flg (numeric)],
8: [8 - region (numeric)],
9: [9 - income (numeric)],
10: [10 - famil... | {'MajorityClassSize': nan,
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'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 1723.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Is-this-a-good-customer | [
"month",
"credit_amount",
"credit_term",
"age",
"sex",
"education",
"product_type",
"having_children_flg",
"region",
"income",
"family_status",
"phone_operator",
"is_client"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 999 |
362,376 | mean_absolute_error | mean_absolute_error | Pima-Indians-Diabetes | DESCRIPTION
Problem Statement
NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) research creates knowledge about and treatments for the most chronic, costly, and consequential diseases.
The dataset used in this project is originally from NIDDK. The objective is to predict whether or not a... | {0: [0 - Pregnancies (numeric)],
1: [1 - Glucose (numeric)],
2: [2 - BloodPressure (numeric)],
3: [3 - SkinThickness (numeric)],
4: [4 - Insulin (numeric)],
5: [5 - BMI (numeric)],
6: [6 - DiabetesPedigreeFunction (numeric)],
7: [7 - Age (numeric)],
8: [8 - Outcome (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 768.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | Pima-Indians-Diabetes | [
"Pregnancies",
"Glucose",
"BloodPressure",
"SkinThickness",
"Insulin",
"BMI",
"DiabetesPedigreeFunction",
"Age"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,000 |
362,398 | mean_absolute_error | mean_absolute_error | AAPL_2022_01 | Apple stock price in the first month of 2022 | {0: [0 - High (numeric)],
1: [1 - Low (numeric)],
2: [2 - Open (numeric)],
3: [3 - Close (numeric)],
4: [4 - Volume (numeric)],
5: [5 - Adj Close (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 22.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | AAPL_2022_01 | [
"High",
"Low",
"Open",
"Volume",
"Adj Close"
] | [
false,
false,
false,
false,
false
] | 1,001 |
362,383 | mean_absolute_error | mean_absolute_error | Heart-Disease-Dataset-(Comprehensive) | Context
Heart Disease Dataset (Most comprehensive)
Content
Heart disease is also known as Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year which is about 32 of all deaths globally. CVDs are a group of disorders of the heart and blood vessels and i... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - chest_pain_type (numeric)],
3: [3 - resting_bp_s (numeric)],
4: [4 - cholesterol (numeric)],
5: [5 - fasting_blood_sugar (numeric)],
6: [6 - resting_ecg (numeric)],
7: [7 - max_heart_rate (numeric)],
8: [8 - exercise_angina (numeric)],
9: [9 - oldpeak (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1190.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Heart-Disease-Dataset-(Comprehensive) | [
"age",
"sex",
"chest_pain_type",
"resting_bp_s",
"cholesterol",
"fasting_blood_sugar",
"resting_ecg",
"max_heart_rate",
"exercise_angina",
"oldpeak",
"ST_slope"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,002 |
362,374 | mean_absolute_error | mean_absolute_error | Pokemon-(Generation-1---Generation-8) | Pokemons are something which fascinated me every single time. Who would believe that a 6 year old kid used to be late to school almost everyday because of watching those extra minutes of the Pokemon episode. Years later, that kid is still deep inside me fantasizing about the Pokemon world.
About the Data
The dataset c... | {0: [0 - Pokedex_No. (numeric)],
1: [1 - Name (string)],
2: [2 - Type (string)],
3: [3 - Other_Type (string)],
4: [4 - HP (numeric)],
5: [5 - Attack (numeric)],
6: [6 - Defense (numeric)],
7: [7 - Special_Attack (numeric)],
8: [8 - Special_Defense (numeric)],
9: [9 - Speed (numeric)],
10: [10 - Total (numeric... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 1045.0,
'NumberOfInstancesWithMissingValues': 492.0,
'NumberOfMissingValues': 492.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
... | Pokemon-(Generation-1---Generation-8) | [
"Pokedex_No.",
"Name",
"Type",
"Other_Type",
"HP",
"Attack",
"Defense",
"Special_Attack",
"Special_Defense",
"Speed",
"Total",
"Generation"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,003 |
362,076 | root_mean_squared_error | root_mean_squared_error | Cancer_Drug_Response_methylation | The dataset is obtained from Qiao Liu et al. (3). | {0: [0 - SKI_1_2159133_2160133 (numeric)],
1: [1 - TNFRSF14_1_2486803_2487803 (numeric)],
2: [2 - PRDM16_1_2984741_2985741 (numeric)],
3: [3 - RPL22_1_6259679_6260679 (numeric)],
4: [4 - CAMTA1_1_6844383_6845383 (numeric)],
5: [5 - MTOR_1_11322608_11323608 (numeric)],
6: [6 - PRDM2_1_14025734_14026734 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 809.0,
'NumberOfInstances': 475.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 809.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | Cancer_Drug_Response_methylation | [
"SKI_1_2159133_2160133",
"TNFRSF14_1_2486803_2487803",
"PRDM16_1_2984741_2985741",
"RPL22_1_6259679_6260679",
"CAMTA1_1_6844383_6845383",
"MTOR_1_11322608_11323608",
"PRDM2_1_14025734_14026734",
"PRDM2_1_14074875_14075875",
"CASP9_1_15850940_15851940",
"CASP9_1_15851285_15852285",
"SPEN_1_161733... | [
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f... | 1,004 |
362,372 | mean_absolute_error | mean_absolute_error | Pima-Indians-Diabetes-Dataset | Context
The unprocessed dataset was acquired from UCI Machine Learning organisation. This dataset is preprocessed by me, originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to accurately predict whether or not, a patient has diabetes, based on multiple f... | {0: [0 - Pregnancies (numeric)],
1: [1 - Glucose (numeric)],
2: [2 - BloodPressure (numeric)],
3: [3 - SkinThickness (numeric)],
4: [4 - Insulin (numeric)],
5: [5 - BMI (numeric)],
6: [6 - DiabetesPedigreeFunction (numeric)],
7: [7 - Age (numeric)],
8: [8 - Outcome (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 768.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | Pima-Indians-Diabetes-Dataset | [
"Pregnancies",
"Glucose",
"BloodPressure",
"SkinThickness",
"Insulin",
"BMI",
"DiabetesPedigreeFunction",
"Age"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,005 |
362,385 | mean_absolute_error | mean_absolute_error | Heart-Disease-Dataset-(Comprehensive) | Context
Heart Disease Dataset (Most comprehensive)
Content
Heart disease also known as Cardiovascular diseases (CVDs) is the number 1 cause of death globally, taking an estimated 17.9 million lives each year which is about 32 of all deaths globally. CVDs are a group of disorders of the heart and blood vessels and inclu... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - chest_pain_type (numeric)],
3: [3 - resting_bp_s (numeric)],
4: [4 - cholesterol (numeric)],
5: [5 - fasting_blood_sugar (numeric)],
6: [6 - resting_ecg (numeric)],
7: [7 - max_heart_rate (numeric)],
8: [8 - exercise_angina (numeric)],
9: [9 - oldpeak (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1190.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Heart-Disease-Dataset-(Comprehensive) | [
"age",
"sex",
"chest_pain_type",
"resting_bp_s",
"cholesterol",
"fasting_blood_sugar",
"resting_ecg",
"max_heart_rate",
"exercise_angina",
"oldpeak",
"ST_slope"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,006 |
362,425 | mean_absolute_error | mean_absolute_error | Heart_disease_classification | This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. The goal field refers to the presence of heart disease in the patient. It is integer valued with 0 or 1. | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - cp (numeric)],
3: [3 - trestpbs (numeric)],
4: [4 - chol (numeric)],
5: [5 - fbs (numeric)],
6: [6 - restecg (numeric)],
7: [7 - thalach (numeric)],
8: [8 - exang (numeric)],
9: [9 - oldpeak (numeric)],
10: [10 - slope (numeric)],
11: [11 - ca (string)... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 296.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Heart_disease_classification | [
"age",
"sex",
"cp",
"trestpbs",
"chol",
"fbs",
"restecg",
"thalach",
"exang",
"oldpeak",
"slope",
"ca",
"thal"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,008 |
362,386 | mean_absolute_error | mean_absolute_error | chronic-kidney-disease | Context
This dataset is originally from UCI Machine Learning Repository. The objective of the dataset is to diagnostically predict whether a patient is having chronic kidney disease or not, based on certain diagnostic measurements included in the dataset.
Content
The datasets consists of several medical predictor varia... | {0: [0 - Bp (numeric)],
1: [1 - Sg (numeric)],
2: [2 - Al (numeric)],
3: [3 - Su (numeric)],
4: [4 - Rbc (numeric)],
5: [5 - Bu (numeric)],
6: [6 - Sc (numeric)],
7: [7 - Sod (numeric)],
8: [8 - Pot (numeric)],
9: [9 - Hemo (numeric)],
10: [10 - Wbcc (numeric)],
11: [11 - Rbcc (numeric)],
12: [12 - Htn (num... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | chronic-kidney-disease | [
"Bp",
"Sg",
"Al",
"Su",
"Rbc",
"Bu",
"Sc",
"Sod",
"Pot",
"Hemo",
"Wbcc",
"Rbcc",
"Htn"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,010 |
362,368 | mean_absolute_error | mean_absolute_error | Boston-house-price-data | Context
This dataset is extracted from the The Boston Housing Dataset, and the extraction of the data is explained in Extract dataset/dataframe from an URL
Acknowledgements
A Dataset derived from information collected by the U.S. Census Service concerning housing in the area of Boston Mass.
Column description:
This dat... | {0: [0 - CRIM (numeric)],
1: [1 - ZN (numeric)],
2: [2 - INDUS (numeric)],
3: [3 - CHAS (numeric)],
4: [4 - NOX (numeric)],
5: [5 - RM (numeric)],
6: [6 - AGE (numeric)],
7: [7 - DIS (numeric)],
8: [8 - RAD (numeric)],
9: [9 - TAX (numeric)],
10: [10 - PTRATIO (numeric)],
11: [11 - B (numeric)],
12: [12 - L... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 506.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Boston-house-price-data | [
"CRIM",
"ZN",
"INDUS",
"CHAS",
"NOX",
"RM",
"AGE",
"DIS",
"RAD",
"TAX",
"PTRATIO",
"B",
"LSTAT"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,011 |
362,419 | mean_absolute_error | mean_absolute_error | 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
] | 1,012 |
362,073 | root_mean_squared_error | root_mean_squared_error | liverTox_ALT_target | The data come from a liver toxicity study in which 64 male rats were exposed to non-toxic (50 or 150 mg/kg),moderately toxic (1500 mg/kg) or severely toxic (2000 mg/kg) doses of acetaminophen (paracetamol) (Bushel, Wolfinger, and Gibson 2007).Necropsy was performed at 6, 18, 24 and 48 hours after exposure and the mRNA ... | {0: [0 - A_43_P14555 (numeric)],
1: [1 - A_43_P22290 (numeric)],
2: [2 - A_43_P20792 (numeric)],
3: [3 - A_43_P21286 (numeric)],
4: [4 - A_43_P12995 (numeric)],
5: [5 - A_43_P15834 (numeric)],
6: [6 - A_43_P12356 (numeric)],
7: [7 - A_42_P564516 (numeric)],
8: [8 - A_43_P22018 (numeric)],
9: [9 - A_43_P21075 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 3117.0,
'NumberOfInstances': 64.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3117.0,
'NumberOfSymbolicFeatures': 0.0,
'... | liverTox_ALT_target | [
"A_43_P14555",
"A_43_P22290",
"A_43_P20792",
"A_43_P21286",
"A_43_P12995",
"A_43_P15834",
"A_43_P12356",
"A_42_P564516",
"A_43_P22018",
"A_43_P21075",
"A_43_P23125",
"A_43_P17653",
"A_43_P18129",
"A_43_P16337",
"A_43_P22160",
"A_43_P23348",
"A_43_P22354",
"A_43_P16280",
"A_43_P13... | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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f... | 1,013 |
362,387 | mean_absolute_error | mean_absolute_error | 1000-Cameras-Dataset | Context
Some camera enthusiast went and described 1,000 cameras based on 13 properties!
Content
Row one describes the datatype for each column and can probably be removed.
The 13 properties of each camera:
Model
Release date
Max resolution
Low resolution
Effective pixels
Zoom wide (W)
Zoom tele (T)
Normal focus range... | {0: [0 - Model (string)],
1: [1 - Release_date (numeric)],
2: [2 - Max_resolution (numeric)],
3: [3 - Low_resolution (numeric)],
4: [4 - Effective_pixels (numeric)],
5: [5 - Zoom_wide_(W) (numeric)],
6: [6 - Zoom_tele_(T) (numeric)],
7: [7 - Normal_focus_range (numeric)],
8: [8 - Macro_focus_range (numeric)],
... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 1038.0,
'NumberOfInstancesWithMissingValues': 2.0,
'NumberOfMissingValues': 7.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | 1000-Cameras-Dataset | [
"Model",
"Release_date",
"Max_resolution",
"Low_resolution",
"Effective_pixels",
"Zoom_wide_(W)",
"Zoom_tele_(T)",
"Normal_focus_range",
"Macro_focus_range",
"Storage_included",
"Weight_(inc._batteries)",
"Dimensions"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,014 |
362,399 | mean_absolute_error | mean_absolute_error | AAPL_2022_01 | Apple stock price in the first month of 2022 | {0: [0 - High (numeric)],
1: [1 - Low (numeric)],
2: [2 - Open (numeric)],
3: [3 - Close (numeric)],
4: [4 - Volume (numeric)],
5: [5 - Adj Close (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 22.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | AAPL_2022_01 | [
"High",
"Low",
"Open",
"Volume",
"Adj Close"
] | [
false,
false,
false,
false,
false
] | 1,015 |
362,443 | mean_absolute_error | mean_absolute_error | 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
] | 1,016 |
362,309 | mean_absolute_error | mean_absolute_error | audit-data | **Author**: Nishtha Hooda, CSED, TIET, Patiala
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Audit+Data) - 2018
**Please cite**: [Hooda, Nishtha, Seema Bawa, and Prashant Singh Rana. 'Fraudulent Firm Classification: A Case Study of an External Audit.' Applied Artificial Intelligence 32.1 (2018): 48-64.]( ... | {0: [0 - Sector_score (numeric)],
1: [1 - LOCATION_ID (string)],
2: [2 - PARA_A (numeric)],
3: [3 - Score_A (numeric)],
4: [4 - Risk_A (numeric)],
5: [5 - PARA_B (numeric)],
6: [6 - Score_B (numeric)],
7: [7 - Risk_B (numeric)],
8: [8 - TOTAL (numeric)],
9: [9 - numbers (numeric)],
10: [10 - Score_B.1 (numeri... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 1552.0,
'NumberOfInstancesWithMissingValues': 1552.0,
'NumberOfMissingValues': 19402.0,
'NumberOfNumericFeatures': 36.0,
'NumberOfSymbolicFeatures': 0.... | audit-data | [
"Sector_score",
"LOCATION_ID",
"PARA_A",
"Score_A",
"Risk_A",
"PARA_B",
"Score_B",
"Risk_B",
"TOTAL",
"numbers",
"Score_B.1",
"Risk_C",
"Money_Value",
"Score_MV",
"Risk_D",
"District_Loss",
"PROB",
"RiSk_E",
"History",
"Prob",
"Risk_F",
"Score",
"Inherent_Risk",
"CONTRO... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
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false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
f... | 1,017 |
362,394 | mean_absolute_error | mean_absolute_error | Another-Dataset-on-used-Fiat-500-(1538-rows) | This dataset has been created from a query done on an website specialized in used cars and contains 1538 rows
Description of colums:
model: Fiat 500 comes in several 'flavours' :'pop', 'lounge', 'sport'
engine_power: number of Kw of the engine
ageindays: age of the car in number of days (from the time the dataset has b... | {0: [0 - model (string)],
1: [1 - engine_power (numeric)],
2: [2 - age_in_days (numeric)],
3: [3 - km (numeric)],
4: [4 - previous_owners (numeric)],
5: [5 - lat (numeric)],
6: [6 - lon (numeric)],
7: [7 - price (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 1538.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Another-Dataset-on-used-Fiat-500-(1538-rows) | [
"model",
"engine_power",
"age_in_days",
"km",
"previous_owners",
"lat",
"lon"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,018 |
362,390 | mean_absolute_error | mean_absolute_error | Goodreads-Computer-Books | Context
The reason for creating this dataset is the requirement of a good clean dataset of computer books. I had searched for datasets on books in Kaggle and I found out that while most of the datasets had a good amount of books listed, there were either major columns missing or grossly unclean data. I mean, you can't ... | {0: [0 - Book_Id (numeric)],
1: [1 - book_Title (string)],
2: [2 - Author_Name (string)],
3: [3 - ratings_count (numeric)],
4: [4 - Avg_Rating (numeric)],
5: [5 - Publish_year (numeric)],
6: [6 - Edition (numeric)],
7: [7 - Pages_no (numeric)],
8: [8 - Book_language (string)],
9: [9 - Reviews (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 1234.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Goodreads-Computer-Books | [
"book_Title",
"Author_Name",
"ratings_count",
"Publish_year",
"Edition",
"Pages_no",
"Book_language",
"Reviews"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,019 |
362,389 | mean_absolute_error | mean_absolute_error | Minneapolis-Air-Quality-Survey | Context
Minneapolis air quality survey results
Content
Contained in the file are Minneapolis air quality survey results obtained between November 2013 and August 2014. The data set was obtained from http://opendata.minneapolismn.gov.
Inspiration
Visualizing air pollutants quantities over the city of Minneapolis may pr... | {0: [0 - X (numeric)],
1: [1 - Y (numeric)],
2: [2 - ObjectID (numeric)],
3: [3 - Date (string)],
4: [4 - Sample_ID (string)],
5: [5 - Parameter (string)],
6: [6 - Results (numeric)],
7: [7 - Units (string)],
8: [8 - CAS (string)],
9: [9 - HRV (numeric)],
10: [10 - Units1 (string)],
11: [11 - HRV_Types (stri... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 18.0,
'NumberOfInstances': 4790.0,
'NumberOfInstancesWithMissingValues': 2953.0,
'NumberOfMissingValues': 2999.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,... | Minneapolis-Air-Quality-Survey | [
"X",
"Y",
"ObjectID",
"Date",
"Sample_ID",
"Parameter",
"Units",
"CAS",
"HRV",
"Units1",
"HRV_Types",
"Name",
"Description",
"Address",
"City_1",
"State",
"Zip"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,020 |
359,947 | root_mean_squared_error | root_mean_squared_error | 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,
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false,
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false,
false,
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false,
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false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
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f... | 1,021 |
362,393 | mean_absolute_error | mean_absolute_error | Performance-Prediction | Content
The Dataset contains the summary of each player.
on the basis of the summary of each player you have to predict the target variable
Target Variable:
1-Signifies whether a player has a career of 5 years or more.
0-Signifies the career of the player is shorter than 5 years.
Some features other than target variab... | {0: [0 - Name (string)],
1: [1 - GamesPlayed (numeric)],
2: [2 - MinutesPlayed (numeric)],
3: [3 - PointsPerGame (numeric)],
4: [4 - FieldGoalsMade (numeric)],
5: [5 - FieldGoalsAttempt (numeric)],
6: [6 - FieldGoalPercent (numeric)],
7: [7 - 3PointMade (numeric)],
8: [8 - 3PointAttempt (numeric)],
9: [9 - 3Po... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 1340.0,
'NumberOfInstancesWithMissingValues': 11.0,
'NumberOfMissingValues': 11.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 0.0,
'... | Performance-Prediction | [
"Name",
"GamesPlayed",
"MinutesPlayed",
"PointsPerGame",
"FieldGoalsMade",
"FieldGoalsAttempt",
"FieldGoalPercent",
"3PointMade",
"3PointAttempt",
"3PointPercent",
"FreeThrowMade",
"FreeThrowAttempt",
"FreeThrowPercent",
"OffensiveRebounds",
"DefensiveRebounds",
"Rebounds",
"Assists"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,022 |
362,361 | mean_absolute_error | mean_absolute_error | Amazon-Echo-Dot-2-Reviews-Dataset | Context
Since Amazon Echo Dot 2 has been the best selling Alexa product, we decided to extract the reviews posted on Amazon for this device. This particular dataset contains reviews posted in September and October 2017. The complete dataset with all the reviews from 2016 can be downloaded from DataStock - a repository ... | {0: [0 - Pageurl (string)],
1: [1 - Title (string)],
2: [2 - Review_Text (string)],
3: [3 - Review_Color (string)],
4: [4 - User_Verified (string)],
5: [5 - Review_Date (string)],
6: [6 - Review_Useful_Count (numeric)],
7: [7 - Configuration_Text (string)],
8: [8 - Rating (numeric)],
9: [9 - Declaration_Text (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 6855.0,
'NumberOfInstancesWithMissingValues': 6854.0,
'NumberOfMissingValues': 13903.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0... | Amazon-Echo-Dot-2-Reviews-Dataset | [
"Pageurl",
"Title",
"Review_Text",
"Review_Color",
"User_Verified",
"Review_Date",
"Review_Useful_Count",
"Configuration_Text",
"Declaration_Text"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,023 |
360,945 | root_mean_squared_error | root_mean_squared_error | 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': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 145.0,
'NumberOfInstances': 1090.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 144.0,
'NumberOfSymbolicFeatures': 1.0,
'... | MIP-2016-regression | [
"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",
"ratio_i_variables",
"ratio_c_variab... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,024 |
362,552 | mean_absolute_error | mean_absolute_error | 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
] | 1,025 |
362,078 | root_mean_squared_error | root_mean_squared_error | Cancer_Drug_Response_copynumber | The dataset is obtained from Qiao Liu et al. (3). | {0: [0 - AKT3 (numeric)],
1: [1 - ABI1 (numeric)],
2: [2 - SH2B3 (numeric)],
3: [3 - CDH10 (numeric)],
4: [4 - CDH11 (numeric)],
5: [5 - AKAP9 (numeric)],
6: [6 - CDH17 (numeric)],
7: [7 - LHFP (numeric)],
8: [8 - CDK4 (numeric)],
9: [9 - CDK6 (numeric)],
10: [10 - OLIG2 (numeric)],
11: [11 - GPHN (numeric)]... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 711.0,
'NumberOfInstances': 475.0,
'NumberOfInstancesWithMissingValues': 126.0,
'NumberOfMissingValues': 252.0,
'NumberOfNumericFeatures': 711.0,
'NumberOfSymbolicFeatures': 0.0,... | Cancer_Drug_Response_copynumber | [
"AKT3",
"ABI1",
"SH2B3",
"CDH10",
"CDH11",
"AKAP9",
"CDH17",
"LHFP",
"CDK4",
"CDK6",
"OLIG2",
"GPHN",
"CDKN1A",
"CDKN1B",
"FSTL3",
"STAG1",
"CDKN2A",
"CDKN2C",
"IKZF1",
"TFG",
"NDRG1",
"CDX2",
"NCOA2",
"CEBPA",
"SLC34A2",
"IGF2BP2",
"CTCF",
"POLQ",
"STAG2",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,026 |
362,342 | mean_absolute_error | mean_absolute_error | IEEE80211aa-GATS | Data shows the downlink goodput for unicast and multicast transmissions with different group sizes and network loads on an IEEE 802.11ac network simulated on NS-3. Different IEEE 802.11aa GATS are used. | {0: [0 - level_0 (numeric)],
1: [1 - multicast_stations (numeric)],
2: [2 - unicast_stations (numeric)],
3: [3 - index (numeric)],
4: [4 - avg_datarate_downlink (numeric)],
5: [5 - avg_datarate_uplink (numeric)],
6: [6 - delay_downlink_multicast (numeric)],
7: [7 - delay_downlink_unicast (numeric)],
8: [8 - del... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 34.0,
'NumberOfInstances': 4881.0,
'NumberOfInstancesWithMissingValues': 691.0,
'NumberOfMissingValues': 691.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
... | IEEE80211aa-GATS | [
"level_0",
"multicast_stations",
"unicast_stations",
"index",
"avg_datarate_downlink",
"avg_datarate_uplink",
"delay_downlink_multicast",
"delay_downlink_unicast",
"delay_uplink",
"gd_downlink_unicast",
"gd_uplink",
"injected_multicast",
"injected_unicast_down",
"injected_unicast_up",
"m... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,027 |
362,557 | mean_absolute_error | mean_absolute_error | 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
] | 1,028 |
362,510 | mean_absolute_error | mean_absolute_error | analcatdata_supreme | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "regression on categorical and
numerical features" benchmark. Original description:
**Author**:
**... | {0: [0 - Actions_taken (numeric)],
1: [1 - Liberal (nominal)],
2: [2 - Unconstitutional (nominal)],
3: [3 - Precedent_alteration (nominal)],
4: [4 - Unanimous (nominal)],
5: [5 - Year_of_decision (numeric)],
6: [6 - Lower_court_disagreement (nominal)],
7: [7 - Log_exposure (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 4052.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
'cost... | analcatdata_supreme | [
"Actions_taken",
"Liberal",
"Unconstitutional",
"Precedent_alteration",
"Unanimous",
"Year_of_decision",
"Lower_court_disagreement"
] | [
false,
true,
true,
true,
true,
false,
true
] | 1,029 |
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