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 |
|---|---|---|---|---|---|---|---|---|---|---|
3,568 | predictive_accuracy | accuracy_score | analcatdata_cyyoung9302 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Year (nominal)],
1: [1 - Pitcher (nominal)],
2: [2 - League (nominal)],
3: [3 - Type (nominal)],
4: [4 - Wins (numeric)],
5: [5 - Win_pct (numeric)],
6: [6 - Saves (numeric)],
7: [7 - ERA (numeric)],
8: [8 - Strikeouts (numeric)],
9: [9 - Innings_pitched (numeric)],
10: [10 - Cy_Young (nominal)]} | {'MajorityClassSize': 73.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 19.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 92.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 4.0,
'cos... | analcatdata_cyyoung9302 | [
"Year",
"League",
"Type",
"Wins",
"Win_pct",
"Saves",
"ERA",
"Strikeouts",
"Innings_pitched"
] | [
true,
true,
true,
false,
false,
false,
false,
false,
false
] | 1,573 |
3,578 | predictive_accuracy | accuracy_score | diggle_table_a2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
DATA-SETS FROM DIGGLE, P.J. (1990). TIME SERIES : A BIOSTATISTICAL
INTRODUCTION. Oxford University Press.
Table: Table A2 Wool prices
Information about the dataset
CLASSTYPE: numeric
CLASSINDEX: none specific | {0: [0 - col_1 (nominal)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (numeric)],
8: [8 - col_9 (numeric)]} | {'MajorityClassSize': 41.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 18.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 310.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | diggle_table_a2 | [
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,574 |
3,569 | predictive_accuracy | accuracy_score | prnn_viruses | **Author**: B.D. Ripley
**Source**: StatLib - Date unknown
**Please cite**:
Dataset from `Pattern Recognition and Neural Networks' by B.D. Ripley. Cambridge University Press (1996) ISBN 0-521-46086-7
The background to the datasets is described in section 1.4; this file relates the computer-readable files to ... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (nominal)],
8: [8 - col_9 (numeric)],
9: [9 - col_10 (nominal)],
10: [10 - col_11 (nominal)],
11: [11 - col_12 (... | {'MajorityClassSize': 39.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 3.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 61.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 9.0,
'co... | prnn_viruses | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9",
"col_10",
"col_11",
"col_12",
"col_13",
"col_14",
"col_15",
"col_16",
"col_17",
"col_18"
] | [
false,
false,
false,
false,
false,
false,
false,
true,
false,
true,
true,
true,
true,
true,
true,
false,
false,
true
] | 1,575 |
3,575 | predictive_accuracy | accuracy_score | sleuth_ex2016 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Contains 110 data sets from the book 'The Statistical Sleuth'
by Fred Ramsey and Dan Schafer; Duxbury Press, 1997.
(schafer@stat.orst.edu) [14/Oct/97] (172k)
Note: description taken from this web site:
http://lib.stat.cmu.edu/datasets/
File: ../... | {0: [0 - sv (nominal)],
1: [1 - ag (nominal)],
2: [2 - tl (numeric)],
3: [3 - ae (numeric)],
4: [4 - wt (numeric)],
5: [5 - bh (numeric)],
6: [6 - hl (numeric)],
7: [7 - fl (numeric)],
8: [8 - tt (numeric)],
9: [9 - sk (numeric)],
10: [10 - kl (numeric)]} | {'MajorityClassSize': 51.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 87.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | sleuth_ex2016 | [
"ag",
"tl",
"ae",
"wt",
"bh",
"hl",
"fl",
"tt",
"sk",
"kl"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,576 |
3,577 | predictive_accuracy | accuracy_score | visualizing_livestock | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This S dump contains 22 data sets from the
book Visualizing Data published by
Hobart Press (books@hobart.com).
The dump was created by data.dump()
and can be read back into S by data.restore().
The name of each S data set is the name of
the data s... | {0: [0 - livestocktype (nominal)],
1: [1 - country (nominal)],
2: [2 - count (numeric)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 26.0,
'MinorityClassSize': 26.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | visualizing_livestock | [
"country",
"count"
] | [
true,
false
] | 1,577 |
3,585 | predictive_accuracy | accuracy_score | veteran | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - treatment (nominal)],
1: [1 - celltype (nominal)],
2: [2 - status (nominal)],
3: [3 - karnofsky (numeric)],
4: [4 - months (numeric)],
5: [5 - age (numeric)],
6: [6 - therapy (nominal)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 94.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 43.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 137.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
'cos... | veteran | [
"treatment",
"celltype",
"status",
"karnofsky",
"months",
"age",
"therapy"
] | [
true,
true,
true,
false,
false,
false,
true
] | 1,578 |
3,574 | predictive_accuracy | accuracy_score | rmftsa_sleepdata | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data Sets for 'Regression Models for Time Series Analysis' by
B. Kedem and K. Fokianos, Wiley 2002. Submitted by Kostas
Fokianos (fokianos@ucy.ac.cy) [8/Nov/02] (176k)
Note: - attribute names were generated manually
- information about data taken... | {0: [0 - heart_rate (numeric)],
1: [1 - sleep_state (nominal)],
2: [2 - temperature (numeric)]} | {'MajorityClassSize': 404.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 94.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1024.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | rmftsa_sleepdata | [
"heart_rate",
"temperature"
] | [
false,
false
] | 1,580 |
3,582 | predictive_accuracy | accuracy_score | fri_c3_100_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 62.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 38.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c3_100_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,581 |
3,587 | predictive_accuracy | accuracy_score | pwLinear | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - a1 (numeric)],
1: [1 - a2 (numeric)],
2: [2 - a3 (numeric)],
3: [3 - a4 (numeric)],
4: [4 - a5 (numeric)],
5: [5 - a6 (numeric)],
6: [6 - a7 (numeric)],
7: [7 - a8 (numeric)],
8: [8 - a9 (numeric)],
9: [9 - a10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 103.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 97.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 200.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'... | pwLinear | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"a7",
"a8",
"a9",
"a10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,582 |
3,571 | predictive_accuracy | accuracy_score | colleges_aaup | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The AAUP dataset for the ASA Statistical Graphics Section's 1995
Data Analysis Exposition contains information on faculty salaries
for 1161 American colleges and universities. The data may be
obtained in either of two formats.
AAUP.DATA contains... | {0: [0 - FICE (numeric)],
1: [1 - College_name (nominal)],
2: [2 - State (nominal)],
3: [3 - Type (nominal)],
4: [4 - Average_salary-full_professors (numeric)],
5: [5 - Average_salary-associate_professors (numeric)],
6: [6 - Average_salary-assistant_professors (numeric)],
7: [7 - Average_salary-all_ranks (numeri... | {'MajorityClassSize': 617.0,
'MaxNominalAttDistinctValues': 52.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 1161.0,
'NumberOfInstancesWithMissingValues': 87.0,
'NumberOfMissingValues': 256.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 2.0... | colleges_aaup | [
"State",
"Average_salary-full_professors",
"Average_salary-associate_professors",
"Average_salary-assistant_professors",
"Average_salary-all_ranks",
"Average_compensation-full_professors",
"Average_compensation-associate_professors",
"Average_compensation-assistant_professors",
"Average_compensation... | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,583 |
3,486 | predictive_accuracy | accuracy_score | spectrometer | **Author**:
**Source**: Unknown - 1988
**Please cite**:
1. Title: Part of the IRAS Low Resolution Spectrometer Database
2. Sources:
(a) Originator: Infra-Red Astronomy Satellite Project Database
(b) Donor: John Stutz <STUTZ@pluto.arc.nasa.gov>
(c) Date: March 1988 (approximately)
3. Past Usage: unknown
... | {0: [0 - LRS-name (nominal)],
1: [1 - LRS-class (nominal)],
2: [2 - ID-type (nominal)],
3: [3 - Right-Ascension (numeric)],
4: [4 - Declination (numeric)],
5: [5 - Scale_Factor (numeric)],
6: [6 - Blue_base_1 (numeric)],
7: [7 - Blue_base_2 (numeric)],
8: [8 - Red_base_1 (numeric)],
9: [9 - Red_base_2 (numeric... | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 531.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 48.0,
'NumberOfFeatures': 102.0,
'NumberOfInstances': 531.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 2.0,... | spectrometer | [
"ID-type",
"Right-Ascension",
"Declination",
"Scale_Factor",
"Blue_base_1",
"Blue_base_2",
"Red_base_1",
"Red_base_2",
"blue-band-flux_1",
"blue-band-flux_2",
"blue-band-flux_3",
"blue-band-flux_4",
"blue-band-flux_5",
"blue-band-flux_6",
"blue-band-flux_7",
"blue-band-flux_8",
"blue... | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
fa... | 1,584 |
3,583 | predictive_accuracy | accuracy_score | rmftsa_ladata | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Total_Mortality (numeric)],
1: [1 - Respiratory_Mortality (numeric)],
2: [2 - Cardiovascular_Mortality (numeric)],
3: [3 - Temperature (numeric)],
4: [4 - Relative_Humidity (numeric)],
5: [5 - Carbon_Monoxide (numeric)],
6: [6 - Sulfur_Dioxideglm.LAshumway (numeric)],
7: [7 - Nitrogen_Dioxide (numeric)]... | {'MajorityClassSize': 286.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 222.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 508.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | rmftsa_ladata | [
"Total_Mortality",
"Respiratory_Mortality",
"Cardiovascular_Mortality",
"Temperature",
"Relative_Humidity",
"Carbon_Monoxide",
"Sulfur_Dioxideglm.LAshumway",
"Nitrogen_Dioxide",
"Hydrocarbons",
"Ozone"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,585 |
3,590 | predictive_accuracy | accuracy_score | analcatdata_vineyard | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Year (nominal)],
1: [1 - Row (numeric)],
2: [2 - Group (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 260.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 208.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 468.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | analcatdata_vineyard | [
"Year",
"Row",
"Group"
] | [
true,
false,
false
] | 1,586 |
3,596 | predictive_accuracy | accuracy_score | fri_c1_250_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 131.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 119.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c1_250_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,587 |
3,592 | predictive_accuracy | accuracy_score | fri_c2_100_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 40.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | fri_c2_100_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,588 |
3,595 | predictive_accuracy | accuracy_score | visualizing_slope | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - error (numeric)],
1: [1 - percent (numeric)],
2: [2 - distance (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 17.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 44.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | visualizing_slope | [
"error",
"percent",
"distance"
] | [
false,
false,
false
] | 1,589 |
3,598 | predictive_accuracy | accuracy_score | fri_c0_250_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 133.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 117.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_250_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,590 |
3,597 | predictive_accuracy | accuracy_score | baskball | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - assists_per_minute (numeric)],
1: [1 - height (numeric)],
2: [2 - time_played (numeric)],
3: [3 - age (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 49.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 96.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | baskball | [
"assists_per_minute",
"height",
"time_played",
"age"
] | [
false,
false,
false,
false
] | 1,591 |
3,599 | predictive_accuracy | accuracy_score | machine_cpu | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - MYCT (numeric)],
1: [1 - MMIN (numeric)],
2: [2 - MMAX (numeric)],
3: [3 - CACH (numeric)],
4: [4 - CHMIN (numeric)],
5: [5 - CHMAX (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 153.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 56.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | machine_cpu | [
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
false,
false,
false,
false,
false,
false
] | 1,592 |
3,589 | predictive_accuracy | accuracy_score | fri_c4_1000_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 547.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 453.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_1000_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,593 |
3,604 | predictive_accuracy | accuracy_score | pharynx | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - 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 - binaryClass (no... | {'MajorityClassSize': 121.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 74.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 195.0,
'NumberOfInstancesWithMissingValues': 2.0,
'NumberOfMissingValues': 2.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 10.0,
'... | pharynx | [
"Inst",
"sex",
"Treatment",
"Grade",
"Age",
"Condition",
"Site",
"T",
"N",
"Status"
] | [
true,
true,
true,
true,
false,
true,
true,
true,
true,
true
] | 1,594 |
3,581 | predictive_accuracy | accuracy_score | fri_c3_1000_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 557.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 443.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_1000_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,596 |
3,605 | predictive_accuracy | accuracy_score | sleep | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - body_weight (numeric)],
1: [1 - brain_weight (numeric)],
2: [2 - max_life_span (numeric)],
3: [3 - gestation_time (numeric)],
4: [4 - predation_index (numeric)],
5: [5 - sleep_exposure_index (numeric)],
6: [6 - danger_index (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 33.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 62.0,
'NumberOfInstancesWithMissingValues': 7.0,
'NumberOfMissingValues': 8.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | sleep | [
"body_weight",
"brain_weight",
"max_life_span",
"gestation_time",
"predation_index",
"sleep_exposure_index",
"danger_index"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,597 |
3,586 | predictive_accuracy | accuracy_score | abalone | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - 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 - binaryClass (nominal)]} | {'MajorityClassSize': 2096.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 2081.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 4177.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 2.0,
... | abalone | [
"Sex",
"Length",
"Diameter",
"Height",
"Whole weight",
"Shucked weight",
"Viscera weight",
"Shell weight"
] | [
true,
false,
false,
false,
false,
false,
false,
false
] | 1,598 |
3,594 | predictive_accuracy | accuracy_score | analcatdata_supreme | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Actions_taken (numeric)],
1: [1 - Liberal (numeric)],
2: [2 - Unconstitutional (numeric)],
3: [3 - Precedent_alteration (numeric)],
4: [4 - Unanimous (numeric)],
5: [5 - Year_of_decision (numeric)],
6: [6 - Lower_court_disagreement (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 3081.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 971.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 4052.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
... | analcatdata_supreme | [
"Actions_taken",
"Liberal",
"Unconstitutional",
"Precedent_alteration",
"Unanimous",
"Year_of_decision",
"Lower_court_disagreement"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,599 |
3,606 | predictive_accuracy | accuracy_score | fri_c3_1000_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 560.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 440.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_1000_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,600 |
3,610 | predictive_accuracy | accuracy_score | fri_c3_250_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 141.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 109.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c3_250_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,601 |
3,603 | predictive_accuracy | accuracy_score | space_ga | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - 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 - binaryClass (nominal)]} | {'MajorityClassSize': 1566.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 1541.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 3107.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | space_ga | [
"ln(VOTES/POP)",
"POP",
"EDUCATION",
"HOUSES",
"INCOME",
"XCOORD"
] | [
false,
false,
false,
false,
false,
false
] | 1,602 |
3,616 | predictive_accuracy | accuracy_score | pm10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - pm10_concentration (numeric)],
1: [1 - cars_per_hour (numeric)],
2: [2 - temperature_at_2m (numeric)],
3: [3 - wind_speed (numeric)],
4: [4 - temperature_diff_2m_25m (numeric)],
5: [5 - wind_direction (numeric)],
6: [6 - hour_of_day (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 254.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 246.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | pm10 | [
"pm10_concentration",
"cars_per_hour",
"temperature_at_2m",
"wind_speed",
"temperature_diff_2m_25m",
"wind_direction",
"hour_of_day"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,603 |
3,611 | predictive_accuracy | accuracy_score | auto_price | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - symboling (nominal)],
1: [1 - normalized-losses (numeric)],
2: [2 - wheel-base (numeric)],
3: [3 - length (numeric)],
4: [4 - width (numeric)],
5: [5 - height (numeric)],
6: [6 - curb-weight (numeric)],
7: [7 - engine-size (numeric)],
8: [8 - bore (numeric)],
9: [9 - stroke (numeric)],
10: [10 - comp... | {'MajorityClassSize': 105.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 54.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 2.0,
'... | auto_price | [
"symboling",
"normalized-losses",
"wheel-base",
"length",
"width",
"height",
"curb-weight",
"engine-size",
"bore",
"stroke",
"compression-ratio",
"horsepower",
"peak-rpm",
"city-mpg",
"highway-mpg"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,604 |
3,608 | predictive_accuracy | accuracy_score | fri_c4_500_100 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 283.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 217.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,... | fri_c4_500_100 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,605 |
3,624 | predictive_accuracy | accuracy_score | analcatdata_election2000 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - County (nominal)],
1: [1 - Gore00 (numeric)],
2: [2 - Bush00 (numeric)],
3: [3 - Buchanan00 (numeric)],
4: [4 - Nader00 (numeric)],
5: [5 - Browne00 (numeric)],
6: [6 - Hagelin00 (numeric)],
7: [7 - Harris00 (numeric)],
8: [8 - McReynolds00 (numeric)],
9: [9 - Moorehead00 (numeric)],
10: [10 - Philli... | {'MajorityClassSize': 49.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 18.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 67.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | analcatdata_election2000 | [
"Gore00",
"Bush00",
"Buchanan00",
"Nader00",
"Browne00",
"Hagelin00",
"Harris00",
"McReynolds00",
"Moorehead00",
"Phillips00",
"Total00",
"Clinton96",
"Dole96",
"Perot96"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,606 |
3,613 | predictive_accuracy | accuracy_score | servo | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - motor (nominal)],
1: [1 - screw (nominal)],
2: [2 - pgain (nominal)],
3: [3 - vgain (nominal)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 129.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 38.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 167.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 5.0,
'co... | servo | [
"motor",
"screw",
"pgain",
"vgain"
] | [
true,
true,
true,
true
] | 1,607 |
3,488 | predictive_accuracy | accuracy_score | yeast_ml8 | **Author**:
**Source**: Unknown -
**Please cite**:
Yeast dataset
Past Usage:
André Elisseeff and Jason Weston.
A kernel method for multi-labelled classification.
In Thomas G. Dietterich, Susan Becker, and Zoubin Ghahramani, editors, Advances in Neural Information Processing Systems 14, 2002. | {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': 2383.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 34.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 117.0,
'NumberOfInstances': 2417.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 103.0,
'NumberOfSymbolicFeatures': 14.... | yeast_ml8 | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6",
"attr7",
"attr8",
"attr9",
"attr10",
"attr11",
"attr12",
"attr13",
"attr14",
"attr15",
"attr16",
"attr17",
"attr18",
"attr19",
"attr20",
"attr21",
"attr22",
"attr23",
"attr24",
"attr25",
"attr26",
"attr27",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,608 |
3,609 | predictive_accuracy | accuracy_score | fri_c1_1000_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 543.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 457.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'... | fri_c1_1000_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,609 |
3,615 | predictive_accuracy | accuracy_score | fri_c3_500_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 263.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 237.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c3_500_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,610 |
3,591 | predictive_accuracy | accuracy_score | bank8FM | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - a1cx (numeric)],
1: [1 - a1cy (numeric)],
2: [2 - b2x (numeric)],
3: [3 - b2y (numeric)],
4: [4 - a2pop (numeric)],
5: [5 - a3pop (numeric)],
6: [6 - temp (numeric)],
7: [7 - mxql (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 4885.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 3307.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
... | bank8FM | [
"a1cx",
"a1cy",
"b2x",
"b2y",
"a2pop",
"a3pop",
"temp",
"mxql"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,611 |
3,612 | predictive_accuracy | accuracy_score | fri_c1_250_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 143.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 107.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_250_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,612 |
3,607 | predictive_accuracy | accuracy_score | rmftsa_sleepdata | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - heart_rate (numeric)],
1: [1 - sleep_state (nominal)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 515.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 509.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1024.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'... | rmftsa_sleepdata | [
"heart_rate",
"sleep_state"
] | [
false,
true
] | 1,613 |
3,614 | predictive_accuracy | accuracy_score | analcatdata_wildcat | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Grievances (numeric)],
1: [1 - Rotate (nominal)],
2: [2 - Union (nominal)],
3: [3 - Workforce (numeric)],
4: [4 - Log_workforce (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 116.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 163.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | analcatdata_wildcat | [
"Grievances",
"Rotate",
"Union",
"Workforce",
"Log_workforce"
] | [
false,
true,
true,
false,
false
] | 1,614 |
3,619 | predictive_accuracy | accuracy_score | wisconsin | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - lymph_node_status (numeric)],
1: [1 - radius_mean (numeric)],
2: [2 - radius_se (numeric)],
3: [3 - radius_worst (numeric)],
4: [4 - texture_mean (numeric)],
5: [5 - texture_se (numeric)],
6: [6 - texture_worst (numeric)],
7: [7 - perimeter_mean (numeric)],
8: [8 - perimeter_se (numeric)],
9: [9 - per... | {'MajorityClassSize': 104.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 90.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 194.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0,
'... | wisconsin | [
"lymph_node_status",
"radius_mean",
"radius_se",
"radius_worst",
"texture_mean",
"texture_se",
"texture_worst",
"perimeter_mean",
"perimeter_se",
"perimeter_worst",
"area_mean",
"area_se",
"area_worst",
"smoothness_mean",
"smoothness_se",
"smoothness_worst",
"compactness_mean",
"co... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,615 |
3,626 | predictive_accuracy | accuracy_score | analcatdata_uktrainacc | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Year (numeric)],
1: [1 - Train_km (numeric)],
2: [2 - Pct_Mark_I (numeric)],
3: [3 - Accidents (numeric)],
4: [4 - SPAD_preventable (numeric)],
5: [5 - Other_preventable (numeric)],
6: [6 - Non_preventable (numeric)],
7: [7 - Year_grouped (numeric)],
8: [8 - Accidents_grouped (numeric)],
9: [9 - SPAD_... | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 4.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 31.0,
'NumberOfInstancesWithMissingValues': 25.0,
'NumberOfMissingValues': 150.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 1.0,
'... | analcatdata_uktrainacc | [
"Train_km",
"Pct_Mark_I",
"Accidents",
"SPAD_preventable",
"Other_preventable",
"Non_preventable",
"Year_grouped",
"Accidents_grouped",
"SPAD_grouped",
"Other_grouped",
"Non_grouped",
"Train_km_grouped",
"Fatalities",
"SPAD_fatalities",
"Other_fatalities"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,616 |
3,617 | predictive_accuracy | accuracy_score | fri_c4_1000_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 560.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 440.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_1000_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,617 |
3,621 | predictive_accuracy | accuracy_score | sleuth_ex1605 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - FMED (numeric)],
1: [1 - TMIQ (numeric)],
2: [2 - Age2IQ (numeric)],
3: [3 - Age4IQ (numeric)],
4: [4 - Age8IQ (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 31.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 31.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 62.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | sleuth_ex1605 | [
"FMED",
"TMIQ",
"Age2IQ",
"Age4IQ",
"Age8IQ"
] | [
false,
false,
false,
false,
false
] | 1,618 |
3,622 | predictive_accuracy | accuracy_score | autoPrice | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - 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': 105.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 54.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 1.0,
'... | 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
] | 1,619 |
3,620 | predictive_accuracy | accuracy_score | fri_c0_100_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 54.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 46.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | fri_c0_100_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,621 |
3,584 | predictive_accuracy | accuracy_score | fri_c4_1000_100 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 564.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 436.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | fri_c4_1000_100 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,622 |
3,601 | predictive_accuracy | accuracy_score | cpu_small | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - runqsz (numeric)],
10: [10 - freemem (numeric)],
11: [11 - freeswa... | {'MajorityClassSize': 5715.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 2477.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 1.0... | cpu_small | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"runqsz",
"freemem",
"freeswap"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,623 |
3,629 | predictive_accuracy | accuracy_score | fri_c0_250_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 125.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 125.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_250_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,624 |
3,637 | predictive_accuracy | accuracy_score | analcatdata_michiganacc | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Time_index (numeric)],
1: [1 - Season (nominal)],
2: [2 - Month (nominal)],
3: [3 - Unemployment_rate (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 48.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 108.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | analcatdata_michiganacc | [
"Season",
"Month",
"Unemployment_rate"
] | [
true,
true,
false
] | 1,625 |
3,628 | predictive_accuracy | accuracy_score | fri_c2_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 45.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c2_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,626 |
3,517 | predictive_accuracy | accuracy_score | ipums_la_97-small | **Author**: IPUMS (ipums@hist.umn.edu)
**Donor**: Stephen Bay (sbay@ics.uci.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/IPUMS+Census+Database) - 1999
**Please cite**:
**IPUMS Database**
This data set contains unweighted PUMS census data from the Los Angeles and Long Beach areas for the y... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (nominal)],
6: [6 - rent (nominal)],
7: [7 - ftotinc (nominal)],
8: [8 - nfams (nominal)],
9: [9 - ncouples (nominal)],
10: [10 - nmothers (nominal)],
11: [11 - nfa... | {'MajorityClassSize': 1938.0,
'MaxNominalAttDistinctValues': 488.0,
'MinorityClassSize': 258.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 7019.0,
'NumberOfInstancesWithMissingValues': 7019.0,
'NumberOfMissingValues': 48089.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeature... | ipums_la_97-small | [
"year",
"gq",
"gqtypeg",
"farm",
"ownershg",
"value",
"rent",
"ftotinc",
"nfams",
"ncouples",
"nmothers",
"nfathers",
"momloc",
"stepmom",
"momrule",
"poploc",
"steppop",
"poprule",
"sploc",
"sprule",
"famsize",
"nchild",
"nchlt5",
"famunit",
"eldch",
"yngch",
"ns... | [
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true,
true,
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true,
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true,
true... | 1,627 |
3,634 | predictive_accuracy | accuracy_score | fri_c3_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 45.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c3_100_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
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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
] | 1,628 |
3,636 | predictive_accuracy | accuracy_score | strikes | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - country_code (numeric)],
1: [1 - year (numeric)],
2: [2 - strike_volume (numeric)],
3: [3 - unemployment (numeric)],
4: [4 - inflation (numeric)],
5: [5 - parliamentary_representation (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 315.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 310.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 625.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | strikes | [
"country_code",
"year",
"strike_volume",
"unemployment",
"inflation",
"parliamentary_representation"
] | [
false,
false,
false,
false,
false,
false
] | 1,629 |
3,623 | predictive_accuracy | accuracy_score | meta | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - 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': 474.0,
'MaxNominalAttDistinctValues': 24.0,
'MinorityClassSize': 54.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 528.0,
'NumberOfInstancesWithMissingValues': 264.0,
'NumberOfMissingValues': 504.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 3.... | meta | [
"DS_Name",
"T",
"N",
"p",
"k",
"Bin",
"Cost",
"SDratio",
"correl",
"cancor1",
"cancor2",
"fract1",
"fract2",
"skewness",
"kurtosis",
"Hc",
"Hx",
"MCx",
"EnAtr",
"NSRatio",
"Alg_Name"
] | [
true,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 1,630 |
360,109 | predictive_accuracy | accuracy_score | kits-subset | Subset of KITS dataset with 100 images | {0: [0 - f0 (numeric)],
1: [1 - f1 (numeric)],
2: [2 - f2 (numeric)],
3: [3 - f3 (numeric)],
4: [4 - f4 (numeric)],
5: [5 - f5 (numeric)],
6: [6 - f6 (numeric)],
7: [7 - f7 (numeric)],
8: [8 - f8 (numeric)],
9: [9 - f9 (numeric)],
10: [10 - f10 (numeric)],
11: [11 - f11 (numeric)],
12: [12 - f12 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 27649.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27649.0,
'NumberOfSymbolicFeatures': 0.0,... | kits-subset | [
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"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",
"f... | [
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f... | 1,631 |
3,632 | predictive_accuracy | accuracy_score | fri_c1_500_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 262.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 238.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_500_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
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false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,632 |
3,630 | predictive_accuracy | accuracy_score | analcatdata_apnea3 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Automatic (nominal)],
1: [1 - Scorer_2 (nominal)],
2: [2 - Subject (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 395.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 55.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 450.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | analcatdata_apnea3 | [
"Automatic",
"Scorer_2",
"Subject"
] | [
true,
true,
false
] | 1,633 |
3,631 | predictive_accuracy | accuracy_score | analcatdata_apnea2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Automatic (nominal)],
1: [1 - Scorer_1 (nominal)],
2: [2 - Subject (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 411.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 64.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 475.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | analcatdata_apnea2 | [
"Automatic",
"Scorer_1",
"Subject"
] | [
true,
true,
false
] | 1,634 |
3,633 | predictive_accuracy | accuracy_score | analcatdata_apnea1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Scorer_1 (nominal)],
1: [1 - Scorer_2 (nominal)],
2: [2 - Subject (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 414.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 61.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 475.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | analcatdata_apnea1 | [
"Scorer_1",
"Scorer_2",
"Subject"
] | [
true,
true,
false
] | 1,635 |
363,482 | mean_absolute_error | mean_absolute_error | coil-20 | From original source:
-----
To database is available in two versions. The first, [unprocessed], consists of images for five of the objects that contain both the object and the background. The second, [processed], contains images for all of the objects in which the background has been discarded (and the images consist ... | {0: [0 - target (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: [1... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 1025.0,
'NumberOfInstances': 1440.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1025.0,
'NumberOfSymbolicFeatures': 0.0,
... | coil-20 | [
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"2",
"3",
"4",
"5",
"6",
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"9",
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"12",
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false,
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f... | 1,636 |
3,640 | predictive_accuracy | accuracy_score | disclosure_x_bias | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Age (numeric)],
1: [1 - Civil (numeric)],
2: [2 - Can/US (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 345.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 317.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 662.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | disclosure_x_bias | [
"Age",
"Civil",
"Can/US"
] | [
false,
false,
false
] | 1,637 |
3,516 | predictive_accuracy | accuracy_score | ipums_la_98-small | **Author**: IPUMS (ipums@hist.umn.edu)
**Donor**: Stephen Bay (sbay@ics.uci.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/IPUMS+Census+Database) - 1999
**Please cite**:
**IPUMS Database**
This data set contains unweighted PUMS census data from the Los Angeles and Long Beach areas for the y... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (nominal)],
6: [6 - rent (nominal)],
7: [7 - ftotinc (nominal)],
8: [8 - nfams (nominal)],
9: [9 - ncouples (nominal)],
10: [10 - nmothers (nominal)],
11: [11 - nfa... | {'MajorityClassSize': 4802.0,
'MaxNominalAttDistinctValues': 3594.0,
'MinorityClassSize': 71.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 7485.0,
'NumberOfInstancesWithMissingValues': 7485.0,
'NumberOfMissingValues': 52048.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeature... | ipums_la_98-small | [
"year",
"gq",
"gqtypeg",
"farm",
"ownershg",
"value",
"rent",
"ftotinc",
"nfams",
"ncouples",
"nmothers",
"nfathers",
"momloc",
"stepmom",
"momrule",
"poploc",
"steppop",
"poprule",
"sploc",
"sprule",
"famsize",
"nchild",
"nchlt5",
"famunit",
"eldch",
"yngch",
"ns... | [
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true,
true,
true,
true,
true,
true,
true,
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true,
true,
true,
true,
true... | 1,638 |
3,648 | predictive_accuracy | accuracy_score | fri_c3_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 40.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c3_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,639 |
3,643 | predictive_accuracy | accuracy_score | sleuth_ex1714 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - zip (numeric)],
1: [1 - fire (numeric)],
2: [2 - theft (numeric)],
3: [3 - age (numeric)],
4: [4 - income (numeric)],
5: [5 - race (numeric)],
6: [6 - vol (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 47.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | sleuth_ex1714 | [
"zip",
"fire",
"theft",
"age",
"income",
"race",
"vol"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,640 |
3,642 | predictive_accuracy | accuracy_score | fri_c0_250_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 125.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 125.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c0_250_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,641 |
3,639 | predictive_accuracy | accuracy_score | fri_c0_250_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 126.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 124.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_250_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,642 |
3,641 | predictive_accuracy | accuracy_score | fri_c2_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 57.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 43.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c2_100_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,643 |
3,635 | predictive_accuracy | accuracy_score | fri_c1_250_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 137.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 113.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_250_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,644 |
3,638 | predictive_accuracy | accuracy_score | quake | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - focal_depth (numeric)],
1: [1 - latitude (numeric)],
2: [2 - longitude (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 1209.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 969.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 2178.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
... | quake | [
"focal_depth",
"latitude",
"longitude"
] | [
false,
false,
false
] | 1,645 |
3,646 | predictive_accuracy | accuracy_score | rabe_265 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 30.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 21.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 51.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | rabe_265 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6"
] | [
false,
false,
false,
false,
false,
false
] | 1,646 |
3,647 | predictive_accuracy | accuracy_score | rabe_266 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 63.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 57.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 120.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | rabe_266 | [
"col_1",
"col_2"
] | [
false,
false
] | 1,647 |
3,645 | predictive_accuracy | accuracy_score | fri_c1_500_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 267.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 233.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_500_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,648 |
3,644 | predictive_accuracy | accuracy_score | bodyfat | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - 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': 128.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 124.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 252.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 1.0,
... | 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
] | 1,649 |
3,651 | predictive_accuracy | accuracy_score | cleveland | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - cp (nominal)],
3: [3 - trestbps (numeric)],
4: [4 - chol (numeric)],
5: [5 - fbs (nominal)],
6: [6 - restecg (nominal)],
7: [7 - thalach (numeric)],
8: [8 - exang (nominal)],
9: [9 - oldpeak (numeric)],
10: [10 - slope (nominal)],
11: [11 - ca (numeric... | {'MajorityClassSize': 164.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 139.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 303.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 6.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 8.0,
'... | cleveland | [
"age",
"sex",
"cp",
"trestbps",
"chol",
"fbs",
"restecg",
"thalach",
"exang",
"oldpeak",
"slope",
"ca",
"thal"
] | [
false,
true,
true,
false,
false,
true,
true,
false,
true,
false,
true,
false,
true
] | 1,650 |
3,654 | predictive_accuracy | accuracy_score | fri_c1_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 53.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c1_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,651 |
3,658 | predictive_accuracy | accuracy_score | fri_c3_250_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 135.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 115.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_250_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,652 |
3,650 | predictive_accuracy | accuracy_score | wind_correlations | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - latitude (numeric)],
1: [1 - longitude (numeric)],
2: [2 - station_1 (numeric)],
3: [3 - station_2 (numeric)],
4: [4 - station_3 (numeric)],
5: [5 - station_4 (numeric)],
6: [6 - station_5 (numeric)],
7: [7 - station_6 (numeric)],
8: [8 - station_7 (numeric)],
9: [9 - station_8 (numeric)],
10: [10 - ... | {'MajorityClassSize': 23.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 22.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 47.0,
'NumberOfInstances': 45.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 46.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | wind_correlations | [
"latitude",
"longitude",
"station_1",
"station_2",
"station_3",
"station_4",
"station_5",
"station_6",
"station_7",
"station_8",
"station_9",
"station_10",
"station_11",
"station_12",
"station_13",
"station_14",
"station_15",
"station_16",
"station_17",
"station_18",
"station... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,653 |
3,649 | predictive_accuracy | accuracy_score | newton_hema | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - id (nominal)],
1: [1 - weeks (numeric)],
2: [2 - cells_percentage (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 70.0,
'MaxNominalAttDistinctValues': 11.0,
'MinorityClassSize': 70.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 140.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | newton_hema | [
"id",
"weeks",
"cells_percentage"
] | [
true,
false,
false
] | 1,654 |
3,653 | predictive_accuracy | accuracy_score | triazines | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - p1_polar (numeric)],
1: [1 - p1_size (numeric)],
2: [2 - p1_flex (numeric)],
3: [3 - p1_h_doner (numeric)],
4: [4 - p1_h_acceptor (numeric)],
5: [5 - p1_pi_doner (numeric)],
6: [6 - p1_pi_acceptor (numeric)],
7: [7 - p1_polarisable (numeric)],
8: [8 - p1_sigma (numeric)],
9: [9 - p1_branch (numeric)],... | {'MajorityClassSize': 109.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 77.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 186.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 60.0,
'NumberOfSymbolicFeatures': 1.0,
'... | triazines | [
"p1_polar",
"p1_size",
"p1_flex",
"p1_h_doner",
"p1_h_acceptor",
"p1_pi_doner",
"p1_pi_acceptor",
"p1_polarisable",
"p1_sigma",
"p1_branch",
"p2_polar",
"p2_size",
"p2_flex",
"p2_h_doner",
"p2_h_acceptor",
"p2_pi_doner",
"p2_pi_acceptor",
"p2_polarisable",
"p2_sigma",
"p2_branc... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,655 |
3,655 | predictive_accuracy | accuracy_score | elusage | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - average_temperature (numeric)],
1: [1 - month (nominal)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 31.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 55.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | elusage | [
"average_temperature",
"month"
] | [
false,
true
] | 1,656 |
3,652 | predictive_accuracy | accuracy_score | witmer_census_1980 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - STATE (nominal)],
1: [1 - OVER65Perc (numeric)],
2: [2 - MEDAGE (numeric)],
3: [3 - PERCAP$ (numeric)],
4: [4 - COLLEGEPerc (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | witmer_census_1980 | [
"OVER65Perc",
"MEDAGE",
"PERCAP$",
"COLLEGEPerc"
] | [
false,
false,
false,
false
] | 1,657 |
3,660 | predictive_accuracy | accuracy_score | disclosure_x_tampered | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Age (numeric)],
1: [1 - Civil (numeric)],
2: [2 - Can/US (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 335.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 327.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 662.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | disclosure_x_tampered | [
"Age",
"Civil",
"Can/US"
] | [
false,
false,
false
] | 1,658 |
3,657 | predictive_accuracy | accuracy_score | fri_c2_500_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 298.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 202.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c2_500_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,659 |
3,656 | predictive_accuracy | accuracy_score | diabetes_numeric | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - age (numeric)],
1: [1 - deficit (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 17.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 43.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | diabetes_numeric | [
"age",
"deficit"
] | [
false,
false
] | 1,661 |
3,661 | predictive_accuracy | accuracy_score | cpu | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - 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 - binaryClass (nominal)]} | {'MajorityClassSize': 156.0,
'MaxNominalAttDistinctValues': 30.0,
'MinorityClassSize': 53.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | cpu | [
"vendor",
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
true,
false,
false,
false,
false,
false,
false
] | 1,662 |
3,666 | predictive_accuracy | accuracy_score | chscase_funds | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (nominal)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 98.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 87.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 185.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | chscase_funds | [
"col_2",
"col_3"
] | [
false,
false
] | 1,663 |
3,513 | predictive_accuracy | accuracy_score | ipums_la_99-small | **Author**: IPUMS (ipums@hist.umn.edu)
**Donor**: Stephen Bay (sbay@ics.uci.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/IPUMS+Census+Database) - 1999
**Please cite**:
**IPUMS Database**
This data set contains unweighted PUMS census data from the Los Angeles and Long Beach areas for the y... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (nominal)],
6: [6 - rent (nominal)],
7: [7 - ftotinc (nominal)],
8: [8 - nfams (nominal)],
9: [9 - ncouples (nominal)],
10: [10 - nmothers (nominal)],
11: [11 - nfa... | {'MajorityClassSize': 5803.0,
'MaxNominalAttDistinctValues': 3890.0,
'MinorityClassSize': 197.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 8844.0,
'NumberOfInstancesWithMissingValues': 8844.0,
'NumberOfMissingValues': 51515.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatur... | ipums_la_99-small | [
"year",
"gq",
"gqtypeg",
"farm",
"ownershg",
"value",
"rent",
"ftotinc",
"nfams",
"ncouples",
"nmothers",
"nfathers",
"momloc",
"stepmom",
"momrule",
"poploc",
"steppop",
"poprule",
"sploc",
"sprule",
"famsize",
"nchild",
"nchlt5",
"famunit",
"eldch",
"yngch",
"ns... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 1,664 |
3,663 | predictive_accuracy | accuracy_score | cholesterol | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - cp (nominal)],
3: [3 - trestbps (numeric)],
4: [4 - fbs (nominal)],
5: [5 - restecg (nominal)],
6: [6 - thalach (numeric)],
7: [7 - exang (nominal)],
8: [8 - oldpeak (numeric)],
9: [9 - slope (nominal)],
10: [10 - ca (numeric)],
11: [11 - thal (nominal... | {'MajorityClassSize': 166.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 137.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 303.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 6.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 8.0,
'... | cholesterol | [
"age",
"sex",
"cp",
"trestbps",
"fbs",
"restecg",
"thalach",
"exang",
"oldpeak",
"slope",
"ca",
"thal",
"num"
] | [
false,
true,
true,
false,
true,
true,
false,
true,
false,
true,
false,
true,
false
] | 1,665 |
3,664 | predictive_accuracy | accuracy_score | fri_c0_1000_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 503.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 497.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'... | fri_c0_1000_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,666 |
3,665 | predictive_accuracy | accuracy_score | pyrim | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - p1_polar (numeric)],
1: [1 - p1_size (numeric)],
2: [2 - p1_flex (numeric)],
3: [3 - p1_h_doner (numeric)],
4: [4 - p1_h_acceptor (numeric)],
5: [5 - p1_pi_doner (numeric)],
6: [6 - p1_pi_acceptor (numeric)],
7: [7 - p1_polarisable (numeric)],
8: [8 - p1_sigma (numeric)],
9: [9 - p2_polar (numeric)],
... | {'MajorityClassSize': 43.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 31.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 28.0,
'NumberOfInstances': 74.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | pyrim | [
"p1_polar",
"p1_size",
"p1_flex",
"p1_h_doner",
"p1_h_acceptor",
"p1_pi_doner",
"p1_pi_acceptor",
"p1_polarisable",
"p1_sigma",
"p2_polar",
"p2_size",
"p2_flex",
"p2_h_doner",
"p2_h_acceptor",
"p2_pi_doner",
"p2_pi_acceptor",
"p2_polarisable",
"p2_sigma",
"p3_polar",
"p3_size",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,667 |
3,669 | predictive_accuracy | accuracy_score | hutsof99_logis | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Age (nominal)],
1: [1 - Gender (nominal)],
2: [2 - Location (nominal)],
3: [3 - Coherence (numeric)],
4: [4 - Maturity (numeric)],
5: [5 - Delay (numeric)],
6: [6 - Prosecute (nominal)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 36.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 34.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 70.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
'cost... | hutsof99_logis | [
"Age",
"Gender",
"Location",
"Coherence",
"Maturity",
"Delay",
"Prosecute"
] | [
true,
true,
true,
false,
false,
false,
true
] | 1,668 |
3,668 | predictive_accuracy | accuracy_score | delta_ailerons | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - RollRate (numeric)],
1: [1 - PitchRate (numeric)],
2: [2 - currPitch (numeric)],
3: [3 - currRoll (numeric)],
4: [4 - diffRollRate (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 3783.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 3346.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 7129.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
... | delta_ailerons | [
"RollRate",
"PitchRate",
"currPitch",
"currRoll",
"diffRollRate"
] | [
false,
false,
false,
false,
false
] | 1,669 |
3,673 | predictive_accuracy | accuracy_score | fri_c0_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 45.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c0_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,670 |
3,627 | predictive_accuracy | accuracy_score | cpu_act | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - pgout (numeric)],
10: [10 - ppgout (numeric)],
11: [11 - pgfree (n... | {'MajorityClassSize': 5715.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 2477.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0... | cpu_act | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"pgout",
"ppgout",
"pgfree",
"pgscan",
"atch",
"pgin",
"ppgin",
"pflt",
"vflt",
"runqsz",
"freemem",
"freeswap"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,671 |
3,676 | predictive_accuracy | accuracy_score | rmftsa_ctoarrivals | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - year (numeric)],
1: [1 - month (nominal)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 163.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 101.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 264.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'... | rmftsa_ctoarrivals | [
"year",
"month"
] | [
false,
true
] | 1,672 |
3,682 | predictive_accuracy | accuracy_score | diggle_table_a1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 25.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 23.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 48.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | diggle_table_a1 | [
"col_1",
"col_2",
"col_3",
"col_4"
] | [
false,
false,
false,
false
] | 1,673 |
3,679 | predictive_accuracy | accuracy_score | chscase_vine2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 256.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 212.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 468.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_vine2 | [
"col_1",
"col_2"
] | [
false,
false
] | 1,674 |
3,683 | predictive_accuracy | accuracy_score | diggle_table_a2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (nominal)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 165.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 145.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 310.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | diggle_table_a2 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8"
] | [
true,
false,
false,
false,
false,
false,
false,
false
] | 1,675 |
3,677 | predictive_accuracy | accuracy_score | fri_c1_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 53.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c1_100_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,676 |
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