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 |
|---|---|---|---|---|---|---|---|---|---|---|
3,680 | predictive_accuracy | accuracy_score | chscase_vine1 | **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 - col_7 (numeric)],
7: [7 - col_8 (numeric)],
8: [8 - col_9 (numeric)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 28.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 52.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | chscase_vine1 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,677 |
3,689 | predictive_accuracy | accuracy_score | fri_c1_500_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': 274.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 226.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_500_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,678 |
3,670 | predictive_accuracy | accuracy_score | fri_c4_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': 264.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 236.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_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... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,679 |
3,675 | predictive_accuracy | accuracy_score | pbc | **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 - D (nominal)],
1: [1 - Z1 (nominal)],
2: [2 - Z2 (numeric)],
3: [3 - Z3 (nominal)],
4: [4 - Z4 (nominal)],
5: [5 - Z5 (nominal)],
6: [6 - Z6 (nominal)],
7: [7 - Z7 (nominal)],
8: [8 - Z8 (numeric)],
9: [9 - Z9 (numeric)],
10: [10 - Z10 (numeric)],
11: [11 - Z11 (numeric)],
12: [12 - Z12 (numeric)],
... | {'MajorityClassSize': 230.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 188.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 418.0,
'NumberOfInstancesWithMissingValues': 142.0,
'NumberOfMissingValues': 1239.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 9... | pbc | [
"D",
"Z1",
"Z2",
"Z3",
"Z4",
"Z5",
"Z6",
"Z7",
"Z8",
"Z9",
"Z10",
"Z11",
"Z12",
"Z13",
"Z14",
"Z15",
"Z16",
"Z17"
] | [
true,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 1,680 |
3,685 | predictive_accuracy | accuracy_score | chatfield_4 | **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 - col_7 (numeric)],
7: [7 - col_8 (numeric)],
8: [8 - col_9 (numeric)],
9: [9 - col_10 (numeric)],
10: [10 - col_11 (numeric)],
11: [11 - col_12 (... | {'MajorityClassSize': 142.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 93.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 235.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 1.0,
'... | chatfield_4 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9",
"col_10",
"col_11",
"col_12"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,681 |
3,691 | predictive_accuracy | accuracy_score | sensory | **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 - Occasion (nominal)],
1: [1 - Judges (nominal)],
2: [2 - Interval (nominal)],
3: [3 - Sittings (nominal)],
4: [4 - Position (nominal)],
5: [5 - Squares (nominal)],
6: [6 - Rows (nominal)],
7: [7 - Columns (nominal)],
8: [8 - Halfplot (nominal)],
9: [9 - Trellis (nominal)],
10: [10 - Method (nominal)],... | {'MajorityClassSize': 337.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 239.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 576.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | sensory | [
"Occasion",
"Judges",
"Interval",
"Sittings",
"Position",
"Squares",
"Rows",
"Columns",
"Halfplot",
"Trellis",
"Method"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,682 |
3,678 | predictive_accuracy | accuracy_score | fri_c3_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': 563.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 437.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'... | fri_c3_1000_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,683 |
3,618 | predictive_accuracy | accuracy_score | puma32H | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - theta1 (numeric)],
1: [1 - theta2 (numeric)],
2: [2 - theta3 (numeric)],
3: [3 - theta4 (numeric)],
4: [4 - theta5 (numeric)],
5: [5 - theta6 (numeric)],
6: [6 - thetad1 (numeric)],
7: [7 - thetad2 (numeric)],
8: [8 - thetad3 (numeric)],
9: [9 - thetad4 (numeric)],
10: [10 - thetad5 (numeric)],
11: ... | {'MajorityClassSize': 4128.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 4064.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0... | puma32H | [
"theta1",
"theta2",
"theta3",
"theta4",
"theta5",
"theta6",
"thetad1",
"thetad2",
"thetad3",
"thetad4",
"thetad5",
"thetad6",
"tau1",
"tau2",
"tau3",
"tau4",
"tau5",
"dm1",
"dm2",
"dm3",
"dm4",
"dm5",
"da1",
"da2",
"da3",
"da4",
"da5",
"db1",
"db2",
"db3",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,684 |
3,696 | predictive_accuracy | accuracy_score | autoMpg | **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 - cylinders (nominal)],
1: [1 - displacement (numeric)],
2: [2 - horsepower (numeric)],
3: [3 - weight (numeric)],
4: [4 - acceleration (numeric)],
5: [5 - model (nominal)],
6: [6 - origin (nominal)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 209.0,
'MaxNominalAttDistinctValues': 13.0,
'MinorityClassSize': 189.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 398.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 6.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 4.0,
'... | autoMpg | [
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
"model",
"origin"
] | [
true,
false,
false,
false,
false,
true,
true
] | 1,685 |
3,692 | predictive_accuracy | accuracy_score | disclosure_x_noise | **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': 333.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 329.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 662.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | disclosure_x_noise | [
"Age",
"Civil",
"Can/US"
] | [
false,
false,
false
] | 1,686 |
3,695 | predictive_accuracy | accuracy_score | fri_c2_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': 159.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 91.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'... | fri_c2_250_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,687 |
3,694 | predictive_accuracy | accuracy_score | fri_c1_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': 55.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 45.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | fri_c1_100_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,688 |
3,672 | predictive_accuracy | accuracy_score | kin8nm | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - theta1 (numeric)],
1: [1 - theta2 (numeric)],
2: [2 - theta3 (numeric)],
3: [3 - theta4 (numeric)],
4: [4 - theta5 (numeric)],
5: [5 - theta6 (numeric)],
6: [6 - theta7 (numeric)],
7: [7 - theta8 (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 4168.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 4024.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
... | kin8nm | [
"theta1",
"theta2",
"theta3",
"theta4",
"theta5",
"theta6",
"theta7",
"theta8"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,689 |
3,701 | predictive_accuracy | accuracy_score | sleuth_case1102 | **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 - brain (numeric)],
1: [1 - liver (numeric)],
2: [2 - time (numeric)],
3: [3 - treat (nominal)],
4: [4 - days (nominal)],
5: [5 - sex (nominal)],
6: [6 - weight (numeric)],
7: [7 - loss (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 19.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 15.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 34.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 4.0,
'cost... | sleuth_case1102 | [
"brain",
"liver",
"time",
"treat",
"days",
"sex",
"weight",
"loss"
] | [
false,
false,
false,
true,
true,
true,
false,
false
] | 1,690 |
3,681 | predictive_accuracy | accuracy_score | puma8NH | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - theta1 (numeric)],
1: [1 - theta2 (numeric)],
2: [2 - theta3 (numeric)],
3: [3 - thetad1 (numeric)],
4: [4 - thetad2 (numeric)],
5: [5 - thetad3 (numeric)],
6: [6 - tau1 (numeric)],
7: [7 - tau2 (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 4114.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 4078.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
... | puma8NH | [
"theta1",
"theta2",
"theta3",
"thetad1",
"thetad2",
"thetad3",
"tau1",
"tau2"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,691 |
3,690 | predictive_accuracy | accuracy_score | boston_corrected | **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 - OBS. (numeric)],
1: [1 - TOWN (nominal)],
2: [2 - TOWN_ID (numeric)],
3: [3 - TRACT (numeric)],
4: [4 - LON (numeric)],
5: [5 - LAT (numeric)],
6: [6 - MEDV (numeric)],
7: [7 - CMEDV (numeric)],
8: [8 - CRIM (numeric)],
9: [9 - ZN (numeric)],
10: [10 - INDUS (numeric)],
11: [11 - CHAS (nominal)],
1... | {'MajorityClassSize': 283.0,
'MaxNominalAttDistinctValues': 92.0,
'MinorityClassSize': 223.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 506.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 4.0,
... | boston_corrected | [
"OBS.",
"TOWN",
"TOWN_ID",
"TRACT",
"LON",
"LAT",
"MEDV",
"CMEDV",
"CRIM",
"ZN",
"INDUS",
"CHAS",
"NOX",
"RM",
"AGE",
"DIS",
"RAD",
"TAX",
"PTRATIO",
"B"
] | [
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
true,
false,
false,
false
] | 1,692 |
3,697 | predictive_accuracy | accuracy_score | fri_c3_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': 139.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 111.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_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
] | 1,693 |
3,693 | predictive_accuracy | accuracy_score | fri_c4_100_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': 53.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_100_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... | 1,694 |
3,684 | predictive_accuracy | accuracy_score | delta_elevators | **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 - climbRate (numeric)],
1: [1 - Altitude (numeric)],
2: [2 - RollRate (numeric)],
3: [3 - curRoll (numeric)],
4: [4 - diffClb (numeric)],
5: [5 - diffDiffClb (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 4785.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 4732.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 9517.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | delta_elevators | [
"climbRate",
"Altitude",
"RollRate",
"curRoll",
"diffClb",
"diffDiffClb"
] | [
false,
false,
false,
false,
false,
false
] | 1,695 |
3,667 | predictive_accuracy | accuracy_score | pbcseq | **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 - case_number (numeric)],
1: [1 - number_of_days (numeric)],
2: [2 - status (numeric)],
3: [3 - drug (nominal)],
4: [4 - age (numeric)],
5: [5 - sex (nominal)],
6: [6 - day (nominal)],
7: [7 - presence_of_asictes (nominal)],
8: [8 - presence_of_hepatomegaly (nominal)],
9: [9 - presence_of_spiders (nomin... | {'MajorityClassSize': 973.0,
'MaxNominalAttDistinctValues': 1024.0,
'MinorityClassSize': 972.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 1945.0,
'NumberOfInstancesWithMissingValues': 832.0,
'NumberOfMissingValues': 1133.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures... | pbcseq | [
"case_number",
"number_of_days",
"status",
"drug",
"age",
"sex",
"day",
"presence_of_asictes",
"presence_of_hepatomegaly",
"presence_of_spiders",
"presence_of_edema",
"serum_bilirubin",
"serum_cholesterol",
"albumin",
"alkaline_phosphatase",
"SGOT",
"platelets",
"prothrombin_time"
... | [
false,
false,
false,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,696 |
3,700 | predictive_accuracy | accuracy_score | analcatdata_vehicle | **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 - Alcohol-related (nominal)],
1: [1 - Gender (nominal)],
2: [2 - Type (nominal)],
3: [3 - Age (nominal)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 21.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 48.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 5.0,
'cost... | analcatdata_vehicle | [
"Alcohol-related",
"Gender",
"Type",
"Age"
] | [
true,
true,
true,
true
] | 1,697 |
3,699 | predictive_accuracy | accuracy_score | fri_c4_250_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': 140.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 110.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,... | fri_c4_250_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... | 1,698 |
3,662 | predictive_accuracy | accuracy_score | fri_c4_1000_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': 560.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 440.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_1000_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... | 1,699 |
3,705 | predictive_accuracy | accuracy_score | autoHorse | **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 - make (nominal)],
3: [3 - fuel-type (nominal)],
4: [4 - aspiration (nominal)],
5: [5 - num-of-doors (numeric)],
6: [6 - body-style (nominal)],
7: [7 - drive-wheels (nominal)],
8: [8 - engine-location (nominal)],
9: [9 - wheel-base (num... | {'MajorityClassSize': 122.0,
'MaxNominalAttDistinctValues': 22.0,
'MinorityClassSize': 83.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 205.0,
'NumberOfInstancesWithMissingValues': 46.0,
'NumberOfMissingValues': 57.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 9.0,... | autoHorse | [
"symboling",
"normalized-losses",
"make",
"fuel-type",
"aspiration",
"num-of-doors",
"body-style",
"drive-wheels",
"engine-location",
"wheel-base",
"length",
"width",
"height",
"curb-weight",
"engine-type",
"num-of-cylinders",
"engine-size",
"fuel-system",
"bore",
"stroke",
"... | [
false,
false,
true,
true,
true,
false,
true,
true,
true,
false,
false,
false,
false,
false,
true,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false
] | 1,700 |
3,715 | predictive_accuracy | accuracy_score | fri_c0_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': 51.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 49.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c0_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... | 1,701 |
3,707 | predictive_accuracy | accuracy_score | analcatdata_runshoes | **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 - Male (nominal)],
1: [1 - Married (nominal)],
2: [2 - Runs.per.week (numeric)],
3: [3 - Age (numeric)],
4: [4 - Income (numeric)],
5: [5 - College (nominal)],
6: [6 - Distance (nominal)],
7: [7 - Treadmill (nominal)],
8: [8 - Miles.per.week (numeric)],
9: [9 - Type.of.running (nominal)],
10: [10 - bin... | {'MajorityClassSize': 36.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 60.0,
'NumberOfInstancesWithMissingValues': 14.0,
'NumberOfMissingValues': 14.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | analcatdata_runshoes | [
"Male",
"Married",
"Runs.per.week",
"Age",
"Income",
"College",
"Distance",
"Treadmill",
"Miles.per.week",
"Type.of.running"
] | [
true,
true,
false,
false,
false,
true,
true,
true,
false,
true
] | 1,702 |
3,709 | predictive_accuracy | accuracy_score | breastTumor | **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 - menopause (nominal)],
2: [2 - inv-nodes (nominal)],
3: [3 - node-caps (nominal)],
4: [4 - deg-malig (nominal)],
5: [5 - breast (nominal)],
6: [6 - breast-quad (nominal)],
7: [7 - irradiation (nominal)],
8: [8 - recurrence (nominal)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 166.0,
'MaxNominalAttDistinctValues': 18.0,
'MinorityClassSize': 120.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 286.0,
'NumberOfInstancesWithMissingValues': 9.0,
'NumberOfMissingValues': 9.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 9.0,
... | breastTumor | [
"age",
"menopause",
"inv-nodes",
"node-caps",
"deg-malig",
"breast",
"breast-quad",
"irradiation",
"recurrence"
] | [
false,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,703 |
3,704 | predictive_accuracy | accuracy_score | kdd_el_nino-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 - buoy (nominal)],
1: [1 - day (nominal)],
2: [2 - latitude (numeric)],
3: [3 - longitude (numeric)],
4: [4 - zon_winds (numeric)],
5: [5 - mer_winds (numeric)],
6: [6 - humidity (numeric)],
7: [7 - air_temp (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 508.0,
'MaxNominalAttDistinctValues': 59.0,
'MinorityClassSize': 274.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 782.0,
'NumberOfInstancesWithMissingValues': 214.0,
'NumberOfMissingValues': 466.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 3.0... | kdd_el_nino-small | [
"buoy",
"day",
"latitude",
"longitude",
"zon_winds",
"mer_winds",
"humidity",
"air_temp"
] | [
true,
true,
false,
false,
false,
false,
false,
false
] | 1,704 |
3,671 | predictive_accuracy | accuracy_score | fri_c3_1000_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': 555.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 445.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_1000_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... | 1,705 |
3,706 | predictive_accuracy | accuracy_score | stock | **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 - company1 (numeric)],
1: [1 - company2 (numeric)],
2: [2 - company3 (numeric)],
3: [3 - company4 (numeric)],
4: [4 - company5 (numeric)],
5: [5 - company6 (numeric)],
6: [6 - company7 (numeric)],
7: [7 - company8 (numeric)],
8: [8 - company9 (numeric)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 488.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 462.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 950.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'... | stock | [
"company1",
"company2",
"company3",
"company4",
"company5",
"company6",
"company7",
"company8",
"company9"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,706 |
3,713 | predictive_accuracy | accuracy_score | schlvote | **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 - vote (nominal)],
1: [1 - tax_rate (numeric)],
2: [2 - budget (numeric)],
3: [3 - budget_change (numeric)],
4: [4 - tax_rate_change (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 28.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 10.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 38.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | schlvote | [
"vote",
"tax_rate",
"budget",
"budget_change",
"tax_rate_change"
] | [
true,
false,
false,
false,
false
] | 1,707 |
3,703 | predictive_accuracy | accuracy_score | fri_c4_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': 284.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 216.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_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
] | 1,708 |
3,717 | predictive_accuracy | accuracy_score | analcatdata_gsssexsurvey | **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 - Married (nominal)],
1: [1 - Age (numeric)],
2: [2 - Years_of_education (numeric)],
3: [3 - Male (nominal)],
4: [4 - Religious (nominal)],
5: [5 - Sex_partners (numeric)],
6: [6 - Income (numeric)],
7: [7 - Drug_use (nominal)],
8: [8 - Same_sex_relations (nominal)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 124.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 35.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 6.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 6.0,
'c... | analcatdata_gsssexsurvey | [
"Married",
"Age",
"Years_of_education",
"Male",
"Religious",
"Sex_partners",
"Income",
"Drug_use",
"Same_sex_relations"
] | [
true,
false,
false,
true,
true,
false,
false,
true,
true
] | 1,709 |
3,723 | predictive_accuracy | accuracy_score | analcatdata_gviolence | **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 - Title (nominal)],
1: [1 - Year (numeric)],
2: [2 - Length (numeric)],
3: [3 - Violence_time (numeric)],
4: [4 - Injuries (numeric)],
5: [5 - Fatal_injuries (numeric)],
6: [6 - Good/neutral_injuries (numeric)],
7: [7 - Good/neutral_fatalities (numeric)],
8: [8 - Bad_injuries (numeric)],
9: [9 - binaryC... | {'MajorityClassSize': 43.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 31.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 74.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | analcatdata_gviolence | [
"Year",
"Length",
"Violence_time",
"Injuries",
"Fatal_injuries",
"Good/neutral_injuries",
"Good/neutral_fatalities",
"Bad_injuries"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,710 |
3,710 | predictive_accuracy | accuracy_score | fri_c0_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': 509.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 491.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_1000_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,711 |
3,720 | predictive_accuracy | accuracy_score | fri_c4_500_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': 276.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 224.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_500_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,712 |
3,719 | predictive_accuracy | accuracy_score | fishcatch | **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 - Species (nominal)],
1: [1 - Length1 (numeric)],
2: [2 - Length2 (numeric)],
3: [3 - Length3 (numeric)],
4: [4 - Height (numeric)],
5: [5 - Width (numeric)],
6: [6 - Sex (nominal)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 95.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 63.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 158.0,
'NumberOfInstancesWithMissingValues': 87.0,
'NumberOfMissingValues': 87.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 3.0,
'c... | fishcatch | [
"Species",
"Length1",
"Length2",
"Length3",
"Height",
"Width",
"Sex"
] | [
true,
false,
false,
false,
false,
false,
true
] | 1,713 |
3,729 | predictive_accuracy | accuracy_score | analcatdata_neavote | **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 - Name (nominal)],
1: [1 - Party (nominal)],
2: [2 - Favorable (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 93.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 7.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | analcatdata_neavote | [
"Party",
"Favorable"
] | [
true,
false
] | 1,714 |
3,725 | predictive_accuracy | accuracy_score | auto93 | **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 - Manufacturer (nominal)],
1: [1 - Type (nominal)],
2: [2 - City_MPG (numeric)],
3: [3 - Highway_MPG (numeric)],
4: [4 - Air_Bags_standard (nominal)],
5: [5 - Drive_train_type (nominal)],
6: [6 - Number_of_cylinders (numeric)],
7: [7 - Engine_size (numeric)],
8: [8 - Horsepower (numeric)],
9: [9 - RPM (... | {'MajorityClassSize': 58.0,
'MaxNominalAttDistinctValues': 31.0,
'MinorityClassSize': 35.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 93.0,
'NumberOfInstancesWithMissingValues': 11.0,
'NumberOfMissingValues': 14.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 7.0,
... | auto93 | [
"Manufacturer",
"Type",
"City_MPG",
"Highway_MPG",
"Air_Bags_standard",
"Drive_train_type",
"Number_of_cylinders",
"Engine_size",
"Horsepower",
"RPM",
"Engine_revolutions_per_mile",
"Manual_transmission_available",
"Fuel_tank_capacity",
"Passenger_capacity",
"Length",
"Wheelbase",
"W... | [
true,
true,
false,
false,
true,
true,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 1,715 |
3,731 | predictive_accuracy | accuracy_score | visualizing_livestock | **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 - livestocktype (nominal)],
1: [1 - country (nominal)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 105.0,
'MaxNominalAttDistinctValues': 26.0,
'MinorityClassSize': 25.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 3.0,
'c... | visualizing_livestock | [
"livestocktype",
"country"
] | [
true,
true
] | 1,716 |
3,721 | predictive_accuracy | accuracy_score | bolts | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
SUMMARY:
Data from an experiment on the affects of machine adjustments on
the time to count bolts. Data appear as the STATS (Issue 10) Challenge.
DATA:
Submitted by W. Robert Stephenson, Iowa State University
email: wrstephe@iastate.edu
A man... | {0: [0 - RUN (numeric)],
1: [1 - SPEED1 (numeric)],
2: [2 - TOTAL (numeric)],
3: [3 - SPEED2 (numeric)],
4: [4 - NUMBER2 (numeric)],
5: [5 - SENS (numeric)],
6: [6 - TIME (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 14.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 40.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | bolts | [
"RUN",
"SPEED1",
"TOTAL",
"SPEED2",
"NUMBER2",
"SENS",
"TIME"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,717 |
3,718 | predictive_accuracy | accuracy_score | boston | **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 - CRIM (numeric)],
1: [1 - ZN (numeric)],
2: [2 - INDUS (numeric)],
3: [3 - CHAS (nominal)],
4: [4 - NOX (numeric)],
5: [5 - RM (numeric)],
6: [6 - AGE (numeric)],
7: [7 - DIS (numeric)],
8: [8 - RAD (numeric)],
9: [9 - TAX (numeric)],
10: [10 - PTRATIO (numeric)],
11: [11 - B (numeric)],
12: [12 - L... | {'MajorityClassSize': 297.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 209.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 506.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 2.0,
... | boston | [
"CRIM",
"ZN",
"INDUS",
"CHAS",
"NOX",
"RM",
"AGE",
"DIS",
"RAD",
"TAX",
"PTRATIO",
"B",
"LSTAT"
] | [
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,718 |
3,722 | predictive_accuracy | accuracy_score | hungarian | **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': 188.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 106.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 294.0,
'NumberOfInstancesWithMissingValues': 293.0,
'NumberOfMissingValues': 782.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 8.0... | hungarian | [
"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
] | 1,719 |
3,727 | predictive_accuracy | accuracy_score | fri_c4_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': 133.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 117.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_250_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,720 |
3,724 | predictive_accuracy | accuracy_score | vinnie | **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 - field_goals (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 195.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 185.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 380.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | vinnie | [
"year",
"field_goals"
] | [
false,
false
] | 1,721 |
3,726 | predictive_accuracy | accuracy_score | sleuth_ex2016 | **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 - 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 - binaryClass (nominal)]} | {'MajorityClassSize': 45.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 42.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 87.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 3.0,
'cos... | sleuth_ex2016 | [
"sv",
"ag",
"tl",
"ae",
"wt",
"bh",
"hl",
"fl",
"tt",
"sk"
] | [
true,
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,722 |
3,733 | predictive_accuracy | accuracy_score | fri_c2_500_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': 286.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 214.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_500_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,723 |
3,728 | predictive_accuracy | accuracy_score | sleuth_ex2015 | **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 - owl (nominal)],
1: [1 - pctring1 (numeric)],
2: [2 - pctring2 (numeric)],
3: [3 - pctring3 (numeric)],
4: [4 - pctring4 (numeric)],
5: [5 - pctring5 (numeric)],
6: [6 - pctring6 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 33.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 27.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 60.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | sleuth_ex2015 | [
"owl",
"pctring1",
"pctring2",
"pctring3",
"pctring4",
"pctring5",
"pctring6"
] | [
true,
false,
false,
false,
false,
false,
false
] | 1,724 |
3,716 | predictive_accuracy | accuracy_score | tecator | **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 - absorbance_1 (numeric)],
1: [1 - absorbance_2 (numeric)],
2: [2 - absorbance_3 (numeric)],
3: [3 - absorbance_4 (numeric)],
4: [4 - absorbance_5 (numeric)],
5: [5 - absorbance_6 (numeric)],
6: [6 - absorbance_7 (numeric)],
7: [7 - absorbance_8 (numeric)],
8: [8 - absorbance_9 (numeric)],
9: [9 - absor... | {'MajorityClassSize': 138.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 102.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 240.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 1.0,... | tecator | [
"absorbance_1",
"absorbance_2",
"absorbance_3",
"absorbance_4",
"absorbance_5",
"absorbance_6",
"absorbance_7",
"absorbance_8",
"absorbance_9",
"absorbance_10",
"absorbance_11",
"absorbance_12",
"absorbance_13",
"absorbance_14",
"absorbance_15",
"absorbance_16",
"absorbance_17",
"a... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,725 |
3,734 | predictive_accuracy | accuracy_score | fri_c1_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': 267.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 233.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c1_500_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,726 |
3,732 | predictive_accuracy | accuracy_score | fri_c4_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': 54.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 46.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c4_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
] | 1,727 |
3,737 | predictive_accuracy | accuracy_score | fri_c3_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': 142.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 108.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_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... | 1,728 |
3,743 | predictive_accuracy | accuracy_score | fri_c2_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': 304.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 196.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_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
] | 1,729 |
3,712 | predictive_accuracy | accuracy_score | wind | **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 (numeric)],
2: [2 - day (numeric)],
3: [3 - RPT (numeric)],
4: [4 - VAL (numeric)],
5: [5 - ROS (numeric)],
6: [6 - KIL (numeric)],
7: [7 - SHA (numeric)],
8: [8 - BIR (numeric)],
9: [9 - DUB (numeric)],
10: [10 - CLA (numeric)],
11: [11 - MUL (numeric)],
12: [12 - CL... | {'MajorityClassSize': 3501.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 3073.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 6574.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 1.0... | wind | [
"year",
"month",
"day",
"RPT",
"VAL",
"ROS",
"KIL",
"SHA",
"BIR",
"DUB",
"CLA",
"MUL",
"CLO",
"BEL"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,730 |
3,714 | predictive_accuracy | accuracy_score | fri_c0_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': 503.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 497.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_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
] | 1,731 |
3,742 | predictive_accuracy | accuracy_score | fri_c4_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_c4_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,732 |
3,735 | predictive_accuracy | accuracy_score | pollen | **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 - RIDGE (numeric)],
1: [1 - NUB (numeric)],
2: [2 - CRACK (numeric)],
3: [3 - WEIGHT (numeric)],
4: [4 - DENSITY (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 1924.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 1924.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 3848.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
... | pollen | [
"RIDGE",
"NUB",
"CRACK",
"WEIGHT",
"DENSITY"
] | [
false,
false,
false,
false,
false
] | 1,734 |
3,739 | predictive_accuracy | accuracy_score | analcatdata_chlamydia | **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 - Race (nominal)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 81.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 19.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 4.0,
'co... | analcatdata_chlamydia | [
"Age",
"Gender",
"Race"
] | [
true,
true,
true
] | 1,735 |
3,747 | predictive_accuracy | accuracy_score | fri_c0_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': 251.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 249.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c0_500_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,736 |
3,749 | predictive_accuracy | accuracy_score | no2 | **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 - no2_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': 251.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 249.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | no2 | [
"no2_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
] | 1,737 |
3,741 | predictive_accuracy | accuracy_score | fri_c2_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_c2_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... | 1,738 |
3,746 | predictive_accuracy | accuracy_score | pollution | **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 - PREC (numeric)],
1: [1 - JANT (numeric)],
2: [2 - JULT (numeric)],
3: [3 - OVR65 (numeric)],
4: [4 - POPN (numeric)],
5: [5 - EDUC (numeric)],
6: [6 - HOUS (numeric)],
7: [7 - DENS (numeric)],
8: [8 - NONW (numeric)],
9: [9 - WWDRK (numeric)],
10: [10 - POOR (numeric)],
11: [11 - HC (numeric)],
12:... | {'MajorityClassSize': 31.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 60.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | pollution | [
"PREC",
"JANT",
"JULT",
"OVR65",
"POPN",
"EDUC",
"HOUS",
"DENS",
"NONW",
"WWDRK",
"POOR",
"HC",
"NOX",
"SO@",
"HUMID"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,739 |
3,740 | predictive_accuracy | accuracy_score | fri_c1_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': 56.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 44.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c1_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... | 1,740 |
3,744 | predictive_accuracy | accuracy_score | mu284 | **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 - LABEL (numeric)],
1: [1 - P85 (numeric)],
2: [2 - P75 (numeric)],
3: [3 - RMT85 (numeric)],
4: [4 - CS82 (numeric)],
5: [5 - SS82 (numeric)],
6: [6 - S82 (numeric)],
7: [7 - ME84 (numeric)],
8: [8 - REV84 (numeric)],
9: [9 - REG (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 142.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 142.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 284.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | mu284 | [
"LABEL",
"P85",
"P75",
"RMT85",
"CS82",
"SS82",
"S82",
"ME84",
"REV84",
"REG"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,741 |
3,758 | predictive_accuracy | accuracy_score | chscase_geyser1 | **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': 134.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 88.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 222.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | chscase_geyser1 | [
"col_1",
"col_2"
] | [
false,
false
] | 1,742 |
3,702 | predictive_accuracy | accuracy_score | fri_c1_1000_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': 547.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 453.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_1000_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... | 1,743 |
3,753 | predictive_accuracy | accuracy_score | cloud | **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 - PERIOD (numeric)],
1: [1 - SEEDED (nominal)],
2: [2 - TE (numeric)],
3: [3 - TW (numeric)],
4: [4 - NC (numeric)],
5: [5 - SC (numeric)],
6: [6 - NWC (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 76.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 32.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 108.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | cloud | [
"PERIOD",
"SEEDED",
"TE",
"TW",
"NC",
"SC",
"NWC"
] | [
false,
true,
false,
false,
false,
false,
false
] | 1,744 |
3,754 | predictive_accuracy | accuracy_score | sleuth_case1202 | **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 - bsal (numeric)],
1: [1 - sal77 (numeric)],
2: [2 - fsex (nominal)],
3: [3 - senior (numeric)],
4: [4 - age (numeric)],
5: [5 - educ (nominal)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 57.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 93.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 3.0,
'cost... | sleuth_case1202 | [
"bsal",
"sal77",
"fsex",
"senior",
"age",
"educ"
] | [
false,
false,
true,
false,
false,
true
] | 1,745 |
3,752 | predictive_accuracy | accuracy_score | fri_c0_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': 50.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 50.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c0_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
] | 1,746 |
3,750 | predictive_accuracy | accuracy_score | mbagrade | **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 - GMAT (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 32.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 61.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | mbagrade | [
"sex",
"GMAT"
] | [
true,
false
] | 1,747 |
3,751 | predictive_accuracy | accuracy_score | fri_c0_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': 256.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 244.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_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... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,748 |
3,756 | predictive_accuracy | accuracy_score | visualizing_hamster | **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 - lung (numeric)],
1: [1 - heart (numeric)],
2: [2 - liver (numeric)],
3: [3 - spleen (numeric)],
4: [4 - kidney (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 33.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 73.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | visualizing_hamster | [
"lung",
"heart",
"liver",
"spleen",
"kidney"
] | [
false,
false,
false,
false,
false
] | 1,749 |
3,755 | predictive_accuracy | accuracy_score | sleuth_case1201 | **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 - sat (numeric)],
2: [2 - takers (numeric)],
3: [3 - income (numeric)],
4: [4 - years (numeric)],
5: [5 - public (numeric)],
6: [6 - expend (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | sleuth_case1201 | [
"sat",
"takers",
"income",
"years",
"public",
"expend"
] | [
false,
false,
false,
false,
false,
false
] | 1,750 |
3,761 | predictive_accuracy | accuracy_score | hip | **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 - control_1 (numeric)],
1: [1 - control_2 (numeric)],
2: [2 - control_3 (numeric)],
3: [3 - control_4 (numeric)],
4: [4 - treatment_1 (numeric)],
5: [5 - treatment_2 (numeric)],
6: [6 - treatment_3 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 28.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 26.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 54.0,
'NumberOfInstancesWithMissingValues': 30.0,
'NumberOfMissingValues': 120.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | hip | [
"control_1",
"control_2",
"control_3",
"control_4",
"treatment_1",
"treatment_2",
"treatment_3"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,751 |
3,768 | predictive_accuracy | accuracy_score | chscase_adopt | **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': 27.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 12.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 39.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | chscase_adopt | [
"col_2",
"col_3"
] | [
false,
false
] | 1,752 |
3,759 | predictive_accuracy | accuracy_score | fri_c3_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': 280.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 220.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_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
] | 1,753 |
3,762 | predictive_accuracy | accuracy_score | analcatdata_negotiation | **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 - Role (nominal)],
1: [1 - Status (numeric)],
2: [2 - Trust (numeric)],
3: [3 - Outcome_favorability (numeric)],
4: [4 - Procedural_fairness (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 66.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 26.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 92.0,
'NumberOfInstancesWithMissingValues': 17.0,
'NumberOfMissingValues': 26.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | analcatdata_negotiation | [
"Role",
"Status",
"Trust",
"Outcome_favorability",
"Procedural_fairness"
] | [
true,
false,
false,
false,
false
] | 1,754 |
3,765 | predictive_accuracy | accuracy_score | sleuth_case2002 | **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 - FM (nominal)],
1: [1 - LC (nominal)],
2: [2 - BK (nominal)],
3: [3 - SS (nominal)],
4: [4 - AG (numeric)],
5: [5 - YR (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 78.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 69.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 147.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 5.0,
'cos... | sleuth_case2002 | [
"FM",
"LC",
"BK",
"SS",
"AG",
"YR"
] | [
true,
true,
true,
true,
false,
false
] | 1,755 |
3,757 | predictive_accuracy | accuracy_score | rabe_148 | **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 - binaryClass (nominal)]} | {'MajorityClassSize': 33.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 33.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 66.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | rabe_148 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5"
] | [
false,
false,
false,
false,
false
] | 1,756 |
3,763 | predictive_accuracy | accuracy_score | chscase_census6 | **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': 235.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 165.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_census6 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6"
] | [
false,
false,
false,
false,
false,
false
] | 1,758 |
3,760 | predictive_accuracy | accuracy_score | colleges_aaup | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - FICE (numeric)],
1: [1 - College_name (nominal)],
2: [2 - State (nominal)],
3: [3 - 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': 813.0,
'MaxNominalAttDistinctValues': 52.0,
'MinorityClassSize': 348.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 1161.0,
'NumberOfInstancesWithMissingValues': 87.0,
'NumberOfMissingValues': 256.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 3... | colleges_aaup | [
"FICE",
"State",
"Type",
"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",
"... | [
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,759 |
3,769 | predictive_accuracy | accuracy_score | chscase_census5 | **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 - col_7 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 207.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 193.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_census5 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,760 |
3,698 | predictive_accuracy | accuracy_score | bank32nh | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - a1cx (numeric)],
1: [1 - a1cy (numeric)],
2: [2 - a1sx (numeric)],
3: [3 - a1sy (numeric)],
4: [4 - a1rho (numeric)],
5: [5 - a1pop (numeric)],
6: [6 - a2cx (numeric)],
7: [7 - a2cy (numeric)],
8: [8 - a2sx (numeric)],
9: [9 - a2sy (numeric)],
10: [10 - a2rho (numeric)],
11: [11 - a2pop (numeric)],
... | {'MajorityClassSize': 5649.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 2543.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0... | bank32nh | [
"a1cx",
"a1cy",
"a1sx",
"a1sy",
"a1rho",
"a1pop",
"a2cx",
"a2cy",
"a2sx",
"a2sy",
"a2rho",
"a2pop",
"a3cx",
"a3cy",
"a3sx",
"a3sy",
"a3rho",
"a3pop",
"temp",
"b1x",
"b1y",
"b1call",
"b1eff",
"b2x",
"b2y",
"b2call",
"b2eff",
"b3x",
"b3y",
"b3call",
"b3eff",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,761 |
3,774 | predictive_accuracy | accuracy_score | fri_c2_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': 140.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 110.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c2_250_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,762 |
3,772 | predictive_accuracy | accuracy_score | chscase_census2 | **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 - col_7 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 203.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 197.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_census2 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,763 |
3,771 | predictive_accuracy | accuracy_score | chscase_census3 | **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 - col_7 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 208.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 192.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_census3 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,764 |
3,770 | predictive_accuracy | accuracy_score | chscase_census4 | **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 - col_7 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 206.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 194.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_census4 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,765 |
3,730 | predictive_accuracy | accuracy_score | fri_c2_1000_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': 582.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 418.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_1000_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... | 1,766 |
3,777 | predictive_accuracy | accuracy_score | balloon | **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 (numeric)], 1: [1 - raw (numeric)], 2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 1519.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 482.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 2001.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 1.0,
... | balloon | [
"raw"
] | [
false
] | 1,767 |
3,784 | predictive_accuracy | accuracy_score | analcatdata_seropositive | **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 - Disease (nominal)],
2: [2 - Positive (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 86.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 46.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 132.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | analcatdata_seropositive | [
"Age",
"Disease",
"Positive"
] | [
false,
true,
false
] | 1,768 |
3,779 | predictive_accuracy | accuracy_score | fri_c3_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': 56.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 44.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | fri_c3_100_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,769 |
3,778 | predictive_accuracy | accuracy_score | plasma_retinol | **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 - SMOKSTAT (nominal)],
3: [3 - QUETELET (numeric)],
4: [4 - VITUSE (nominal)],
5: [5 - CALORIES (numeric)],
6: [6 - FAT (numeric)],
7: [7 - FIBER (numeric)],
8: [8 - ALCOHOL (numeric)],
9: [9 - CHOLESTEROL (numeric)],
10: [10 - BETADIET (numeric)],
11: [... | {'MajorityClassSize': 182.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 133.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 315.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 4.0,
... | plasma_retinol | [
"AGE",
"SEX",
"SMOKSTAT",
"QUETELET",
"VITUSE",
"CALORIES",
"FAT",
"FIBER",
"ALCOHOL",
"CHOLESTEROL",
"BETADIET",
"RETDIET",
"BETAPLASMA"
] | [
false,
true,
true,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,770 |
3,775 | predictive_accuracy | accuracy_score | fri_c2_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': 584.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 416.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'... | fri_c2_1000_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,771 |
3,788 | predictive_accuracy | accuracy_score | visualizing_galaxy | **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 - eastwest (numeric)],
1: [1 - northsouth (numeric)],
2: [2 - angle (numeric)],
3: [3 - radialposition (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 175.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 148.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 323.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | visualizing_galaxy | [
"eastwest",
"northsouth",
"angle",
"radialposition"
] | [
false,
false,
false,
false
] | 1,772 |
3,782 | predictive_accuracy | accuracy_score | rabe_166 | **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': 21.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 19.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 40.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | rabe_166 | [
"col_2"
] | [
false
] | 1,773 |
3,773 | predictive_accuracy | accuracy_score | fri_c1_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': 564.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 436.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_1000_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,774 |
3,781 | predictive_accuracy | accuracy_score | fri_c4_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': 135.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 115.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_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... | 1,775 |
3,783 | predictive_accuracy | accuracy_score | fri_c2_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': 295.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 205.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_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... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,776 |
3,790 | predictive_accuracy | accuracy_score | hutsof99_child_witness | **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 - subj (numeric)],
1: [1 - Q1 (numeric)],
2: [2 - Q2 (numeric)],
3: [3 - Q3 (numeric)],
4: [4 - Q4 (numeric)],
5: [5 - Q5 (numeric)],
6: [6 - Q6 (numeric)],
7: [7 - Q7 (numeric)],
8: [8 - Q8 (numeric)],
9: [9 - Q9 (numeric)],
10: [10 - Q10 (numeric)],
11: [11 - Q11 (numeric)],
12: [12 - Q12 (numeric)... | {'MajorityClassSize': 25.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 17.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 42.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | hutsof99_child_witness | [
"Q1",
"Q2",
"Q3",
"Q4",
"Q5",
"Q6",
"Q7",
"Q8",
"Q9",
"Q10",
"Q11",
"Q12",
"Q13",
"Q14",
"comp"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,777 |
3,787 | predictive_accuracy | accuracy_score | humandevel | **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 - rank (numeric)],
1: [1 - country (nominal)],
2: [2 - hdi (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 65.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 65.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | humandevel | [
"hdi"
] | [
false
] | 1,778 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.