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 |
|---|---|---|---|---|---|---|---|---|---|---|
3,766 | predictive_accuracy | accuracy_score | fri_c2_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': 563.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 437.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_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
] | 1,779 |
3,776 | predictive_accuracy | accuracy_score | fri_c2_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': 580.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 420.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_1000_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,780 |
3,785 | predictive_accuracy | accuracy_score | fri_c2_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': 58.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 42.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c2_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... | 1,781 |
3,794 | predictive_accuracy | accuracy_score | disclosure_z | **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': 348.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 314.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 662.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | disclosure_z | [
"Age",
"Civil",
"Can/US"
] | [
false,
false,
false
] | 1,782 |
3,791 | predictive_accuracy | accuracy_score | rabe_97 | **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 (nominal)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 25.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 21.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 46.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | rabe_97 | [
"col_2",
"col_3",
"col_4"
] | [
false,
false,
true
] | 1,783 |
3,789 | predictive_accuracy | accuracy_score | fri_c0_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': 255.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 245.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_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
] | 1,784 |
3,780 | predictive_accuracy | accuracy_score | fri_c1_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': 546.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 454.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_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
] | 1,785 |
3,792 | predictive_accuracy | accuracy_score | rabe_176 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 35.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 35.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 70.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | rabe_176 | [
"col_2",
"col_3",
"col_4"
] | [
false,
false,
false
] | 1,786 |
3,797 | predictive_accuracy | accuracy_score | socmob | **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 - fathers_occupation (nominal)],
1: [1 - sons_occupation (nominal)],
2: [2 - family_structure (nominal)],
3: [3 - race (nominal)],
4: [4 - counts_for_sons_first_occupation (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 900.0,
'MaxNominalAttDistinctValues': 17.0,
'MinorityClassSize': 256.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 1156.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 5.0,
... | socmob | [
"fathers_occupation",
"sons_occupation",
"family_structure",
"race",
"counts_for_sons_first_occupation"
] | [
true,
true,
true,
true,
false
] | 1,787 |
3,767 | predictive_accuracy | accuracy_score | fri_c0_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': 510.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 490.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_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... | 1,788 |
3,786 | predictive_accuracy | accuracy_score | visualizing_soil | **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 - northing (numeric)],
1: [1 - easting (numeric)],
2: [2 - resistivity (numeric)],
3: [3 - isns (nominal)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 4753.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 3888.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 8641.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 2.0,
... | visualizing_soil | [
"northing",
"easting",
"resistivity",
"isns"
] | [
false,
false,
false,
true
] | 1,789 |
3,801 | predictive_accuracy | accuracy_score | sleuth_ex1221 | **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 - river (nominal)],
1: [1 - country (nominal)],
2: [2 - discharge (numeric)],
3: [3 - runoff (numeric)],
4: [4 - area (numeric)],
5: [5 - density (numeric)],
6: [6 - no3 (numeric)],
7: [7 - export (numeric)],
8: [8 - dep (numeric)],
9: [9 - nprec (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 23.0,
'MaxNominalAttDistinctValues': 26.0,
'MinorityClassSize': 19.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 42.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | sleuth_ex1221 | [
"country",
"discharge",
"runoff",
"area",
"density",
"no3",
"export",
"dep",
"nprec"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,790 |
3,799 | predictive_accuracy | accuracy_score | fri_c3_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': 272.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 228.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_500_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,791 |
3,802 | predictive_accuracy | accuracy_score | chscase_whale | **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_6 (numeric)],
5: [5 - col_7 (numeric)],
6: [6 - col_8 (numeric)],
7: [7 - col_9 (numeric)],
8: [8 - col_10 (numeric)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 117.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 111.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 228.0,
'NumberOfInstancesWithMissingValues': 5.0,
'NumberOfMissingValues': 20.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'... | chscase_whale | [
"col_2",
"col_3",
"col_4",
"col_6",
"col_7",
"col_8",
"col_9",
"col_10"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,792 |
3,795 | predictive_accuracy | accuracy_score | fri_c4_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_c4_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... | 1,793 |
3,800 | predictive_accuracy | accuracy_score | fri_c3_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': 282.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 218.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_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... | 1,794 |
3,804 | predictive_accuracy | accuracy_score | lowbwt | **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 - LOW (nominal)],
1: [1 - AGE (numeric)],
2: [2 - LWT (numeric)],
3: [3 - RACE (nominal)],
4: [4 - SMOKE (nominal)],
5: [5 - PTL (nominal)],
6: [6 - HT (nominal)],
7: [7 - UI (nominal)],
8: [8 - FTV (nominal)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 99.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 90.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 189.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 8.0,
'co... | lowbwt | [
"LOW",
"AGE",
"LWT",
"RACE",
"SMOKE",
"PTL",
"HT",
"UI",
"FTV"
] | [
true,
false,
false,
true,
true,
true,
true,
true,
true
] | 1,795 |
3,809 | predictive_accuracy | accuracy_score | visualizing_ethanol | **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 - NOx (numeric)], 1: [1 - C (numeric)], 2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 45.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 43.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 88.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | visualizing_ethanol | [
"NOx",
"C"
] | [
false,
false
] | 1,796 |
362,299 | mean_absolute_error | mean_absolute_error | kits-subset | Subset of KITS dataset with 100 images | {0: [0 - f0 (numeric)],
1: [1 - f1 (numeric)],
2: [2 - f2 (numeric)],
3: [3 - f3 (numeric)],
4: [4 - f4 (numeric)],
5: [5 - f5 (numeric)],
6: [6 - f6 (numeric)],
7: [7 - f7 (numeric)],
8: [8 - f8 (numeric)],
9: [9 - f9 (numeric)],
10: [10 - f10 (numeric)],
11: [11 - f11 (numeric)],
12: [12 - f12 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 27649.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27649.0,
'NumberOfSymbolicFeatures': 0.0,... | kits-subset | [
"f0",
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"f7",
"f8",
"f9",
"f10",
"f11",
"f12",
"f13",
"f14",
"f15",
"f16",
"f17",
"f18",
"f19",
"f20",
"f21",
"f22",
"f23",
"f24",
"f25",
"f26",
"f27",
"f28",
"f29",
"f30",
"f31",
"f32",
"f33",
"f34",
"f35",
"f... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,797 |
3,807 | predictive_accuracy | accuracy_score | echoMonths | **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 - still_alive (nominal)],
1: [1 - age (numeric)],
2: [2 - pericardial (nominal)],
3: [3 - fractional (numeric)],
4: [4 - epss (numeric)],
5: [5 - lvdd (numeric)],
6: [6 - wall_score (numeric)],
7: [7 - wall_index (numeric)],
8: [8 - alive_at_1 (nominal)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 66.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 64.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 69.0,
'NumberOfMissingValues': 97.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 4.0,
'... | echoMonths | [
"still_alive",
"age",
"pericardial",
"fractional",
"epss",
"lvdd",
"wall_score",
"wall_index",
"alive_at_1"
] | [
true,
false,
true,
false,
false,
false,
false,
false,
true
] | 1,798 |
3,796 | predictive_accuracy | accuracy_score | fri_c4_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': 136.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 114.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_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
] | 1,799 |
3,810 | predictive_accuracy | accuracy_score | arsenic-male-bladder | **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 - group (nominal)],
1: [1 - conc (numeric)],
2: [2 - age (numeric)],
3: [3 - at.risk (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 535.0,
'MaxNominalAttDistinctValues': 43.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 559.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | arsenic-male-bladder | [
"group",
"conc",
"age",
"at.risk"
] | [
true,
false,
false,
false
] | 1,800 |
4,226 | predictive_accuracy | accuracy_score | oh15.wc | null | {0: [0 - cluster (numeric)],
1: [1 - infus (numeric)],
2: [2 - gland (numeric)],
3: [3 - dopamin (numeric)],
4: [4 - phagocytosi (numeric)],
5: [5 - fetal (numeric)],
6: [6 - signific (numeric)],
7: [7 - penetr (numeric)],
8: [8 - hepat (numeric)],
9: [9 - cigarett (numeric)],
10: [10 - fusion (numeric)],
11... | {'MajorityClassSize': 157.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 53.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 3101.0,
'NumberOfInstances': 913.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3100.0,
'NumberOfSymbolicFeatures': 1... | oh15.wc | [
"cluster",
"infus",
"gland",
"dopamin",
"phagocytosi",
"fetal",
"signific",
"penetr",
"hepat",
"cigarett",
"fusion",
"nitroprussid",
"rifampin",
"resist",
"huvec",
"rest",
"quadricep",
"goal",
"hydroxi",
"nucleotid",
"echocardiographi",
"agent",
"0",
"placem",
"juli",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,801 |
4,231 | predictive_accuracy | accuracy_score | re0.wc | null | {0: [0 - depart (numeric)],
1: [1 - sloane (numeric)],
2: [2 - sudden (numeric)],
3: [3 - imbal (numeric)],
4: [4 - subsidis (numeric)],
5: [5 - mccarthi (numeric)],
6: [6 - signific (numeric)],
7: [7 - penetr (numeric)],
8: [8 - announc (numeric)],
9: [9 - fairli (numeric)],
10: [10 - jointli (numeric)],
11... | {'MajorityClassSize': 608.0,
'MaxNominalAttDistinctValues': 13.0,
'MinorityClassSize': 11.0,
'NumberOfClasses': 13.0,
'NumberOfFeatures': 2887.0,
'NumberOfInstances': 1504.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2886.0,
'NumberOfSymbolicFeatures': ... | re0.wc | [
"depart",
"sloane",
"sudden",
"imbal",
"subsidis",
"mccarthi",
"signific",
"penetr",
"announc",
"fairli",
"jointli",
"resist",
"rival",
"rest",
"agenc",
"goal",
"imperi",
"decision",
"drain",
"agent",
"0",
"lord",
"merchant",
"curtail",
"juli",
"varieti",
"partner... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,802 |
4,232 | predictive_accuracy | accuracy_score | oh0.wc | null | {0: [0 - depart (numeric)],
1: [1 - cluster (numeric)],
2: [2 - nephropathi (numeric)],
3: [3 - sudden (numeric)],
4: [4 - infus (numeric)],
5: [5 - gland (numeric)],
6: [6 - dopamin (numeric)],
7: [7 - fetal (numeric)],
8: [8 - signific (numeric)],
9: [9 - penetr (numeric)],
10: [10 - hepat (numeric)],
11: ... | {'MajorityClassSize': 194.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 51.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 3183.0,
'NumberOfInstances': 1003.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3182.0,
'NumberOfSymbolicFeatures': ... | oh0.wc | [
"depart",
"cluster",
"nephropathi",
"sudden",
"infus",
"gland",
"dopamin",
"fetal",
"signific",
"penetr",
"hepat",
"cigarett",
"fairli",
"resist",
"agenc",
"rest",
"seropreval",
"goal",
"nucleotid",
"hydroxi",
"echocardiographi",
"decision",
"agent",
"0",
"tongue",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,803 |
4,234 | predictive_accuracy | accuracy_score | oh5.wc | **Author**: [George Forman](https://scholar.google.com/citations?user=r0a222QAAAAJ)
**Source**: http://tunedit.org/repo/Data/Text-wc/oh5.wc.arff
**Please cite**: | {0: [0 - depart (numeric)],
1: [1 - nephropathi (numeric)],
2: [2 - cluster (numeric)],
3: [3 - tenth (numeric)],
4: [4 - sudden (numeric)],
5: [5 - infus (numeric)],
6: [6 - imbal (numeric)],
7: [7 - gland (numeric)],
8: [8 - cyclophosphamid (numeric)],
9: [9 - furth (numeric)],
10: [10 - phagocytosi (numeri... | {'MajorityClassSize': 149.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 59.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 3013.0,
'NumberOfInstances': 918.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3012.0,
'NumberOfSymbolicFeatures': 1... | oh5.wc | [
"depart",
"nephropathi",
"cluster",
"tenth",
"sudden",
"infus",
"imbal",
"gland",
"cyclophosphamid",
"furth",
"phagocytosi",
"fetal",
"signific",
"penetr",
"hepat",
"cigarett",
"fusion",
"resist",
"rest",
"goal",
"hypothyroid",
"posttraumat",
"hydroxi",
"echocardiograph... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,804 |
3,798 | predictive_accuracy | accuracy_score | fri_c1_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': 140.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 110.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_250_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,805 |
3,805 | predictive_accuracy | accuracy_score | chscase_health | **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 (nominal)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | chscase_health | [
"col_2",
"col_3",
"col_4"
] | [
true,
false,
false
] | 1,806 |
4,241 | predictive_accuracy | accuracy_score | oh10.wc | null | {0: [0 - sudden (numeric)],
1: [1 - gland (numeric)],
2: [2 - signific (numeric)],
3: [3 - penetr (numeric)],
4: [4 - hepat (numeric)],
5: [5 - fusion (numeric)],
6: [6 - agenc (numeric)],
7: [7 - rest (numeric)],
8: [8 - seropreval (numeric)],
9: [9 - nucleotid (numeric)],
10: [10 - echocardiographi (numeric... | {'MajorityClassSize': 165.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 52.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 3239.0,
'NumberOfInstances': 1050.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3238.0,
'NumberOfSymbolicFeatures': ... | oh10.wc | [
"sudden",
"gland",
"signific",
"penetr",
"hepat",
"fusion",
"agenc",
"rest",
"seropreval",
"nucleotid",
"echocardiographi",
"decision",
"agent",
"0",
"placem",
"environ",
"obstetr",
"vagin",
"overview",
"cytokin",
"reconstitut",
"discharg",
"f",
"ileal",
"g",
"clind... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,807 |
4,235 | predictive_accuracy | accuracy_score | re1.wc | null | {0: [0 - imbal (numeric)],
1: [1 - subsidis (numeric)],
2: [2 - signific (numeric)],
3: [3 - fairli (numeric)],
4: [4 - rest (numeric)],
5: [5 - agenc (numeric)],
6: [6 - violent (numeric)],
7: [7 - francais (numeric)],
8: [8 - chuck (numeric)],
9: [9 - decision (numeric)],
10: [10 - agent (numeric)],
11: [1... | {'MajorityClassSize': 371.0,
'MaxNominalAttDistinctValues': 25.0,
'MinorityClassSize': 10.0,
'NumberOfClasses': 25.0,
'NumberOfFeatures': 3759.0,
'NumberOfInstances': 1657.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3758.0,
'NumberOfSymbolicFeatures': ... | re1.wc | [
"imbal",
"subsidis",
"signific",
"fairli",
"rest",
"agenc",
"violent",
"francais",
"chuck",
"decision",
"agent",
"0",
"tower",
"margarin",
"placem",
"curtail",
"placer",
"environ",
"hamburg",
"und",
"fly",
"panamanian",
"housew",
"pension",
"discharg",
"e",
"drake... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,808 |
3,814 | predictive_accuracy | accuracy_score | arsenic-male-lung | **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 - group (nominal)],
1: [1 - conc (numeric)],
2: [2 - age (numeric)],
3: [3 - at.risk (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 546.0,
'MaxNominalAttDistinctValues': 43.0,
'MinorityClassSize': 13.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 559.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | arsenic-male-lung | [
"group",
"conc",
"age",
"at.risk"
] | [
true,
false,
false,
false
] | 1,809 |
3,812 | predictive_accuracy | accuracy_score | arsenic-female-bladder | **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 - group (nominal)],
1: [1 - conc (numeric)],
2: [2 - age (numeric)],
3: [3 - at.risk (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 479.0,
'MaxNominalAttDistinctValues': 43.0,
'MinorityClassSize': 80.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 559.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | arsenic-female-bladder | [
"group",
"conc",
"age",
"at.risk"
] | [
true,
false,
false,
false
] | 1,810 |
4,224 | predictive_accuracy | accuracy_score | tr21.wc | null | {0: [0 - noteworthi (numeric)],
1: [1 - parad (numeric)],
2: [2 - naval (numeric)],
3: [3 - ward (numeric)],
4: [4 - dock (numeric)],
5: [5 - logist (numeric)],
6: [6 - tonnag (numeric)],
7: [7 - maine (numeric)],
8: [8 - ap (numeric)],
9: [9 - sallie (numeric)],
10: [10 - franci (numeric)],
11: [11 - libert... | {'MajorityClassSize': 231.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 4.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 7903.0,
'NumberOfInstances': 336.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7902.0,
'NumberOfSymbolicFeatures': 1.0,... | tr21.wc | [
"noteworthi",
"parad",
"naval",
"ward",
"dock",
"logist",
"tonnag",
"maine",
"ap",
"sallie",
"franci",
"libertarian",
"pip",
"russell",
"sanford",
"approp",
"ind",
"shipboard",
"sunset",
"servant",
"depart",
"logansport",
"number",
"english",
"maritim",
"signific",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,811 |
4,237 | predictive_accuracy | accuracy_score | tr12.wc | null | {0: [0 - saga (numeric)],
1: [1 - stumble (numeric)],
2: [2 - abu (numeric)],
3: [3 - membership (numeric)],
4: [4 - rc (numeric)],
5: [5 - cherish (numeric)],
6: [6 - airwai (numeric)],
7: [7 - concur (numeric)],
8: [8 - ravag (numeric)],
9: [9 - perestroyka (numeric)],
10: [10 - rf (numeric)],
11: [11 - 0 ... | {'MajorityClassSize': 93.0,
'MaxNominalAttDistinctValues': 8.0,
'MinorityClassSize': 9.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 5805.0,
'NumberOfInstances': 313.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5804.0,
'NumberOfSymbolicFeatures': 1.0,
... | tr12.wc | [
"saga",
"stumble",
"abu",
"membership",
"rc",
"cherish",
"airwai",
"concur",
"ravag",
"perestroyka",
"rf",
"0",
"environ",
"purchas",
"loss",
"lost",
"manag",
"particip",
"southeast",
"product",
"dinner",
"anglo",
"aqueou",
"deton",
"conspiraci",
"ethic",
"criteri... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,812 |
3,806 | predictive_accuracy | accuracy_score | fri_c0_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': 259.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 241.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_500_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,813 |
3,815 | predictive_accuracy | accuracy_score | prnn_fglass | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Datasets for `Pattern Recognition and Neural Networks' by B.D. Ripley
=====================================================================
Cambridge University Press (1996) ISBN 0-521-46086-7
The background to the datasets is described in sec... | {0: [0 - RI (numeric)],
1: [1 - Na (numeric)],
2: [2 - Mg (numeric)],
3: [3 - Al (numeric)],
4: [4 - Si (numeric)],
5: [5 - K (numeric)],
6: [6 - Ca (numeric)],
7: [7 - Ba (numeric)],
8: [8 - Fe (numeric)],
9: [9 - type (nominal)]} | {'MajorityClassSize': 76.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 9.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 214.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | prnn_fglass | [
"RI",
"Na",
"Mg",
"Al",
"Si",
"K",
"Ca",
"Ba",
"Fe"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,814 |
4,228 | predictive_accuracy | accuracy_score | tr23.wc | null | {0: [0 - utmost (numeric)],
1: [1 - secondli (numeric)],
2: [2 - neglect (numeric)],
3: [3 - shalala (numeric)],
4: [4 - weekend (numeric)],
5: [5 - timefram (numeric)],
6: [6 - formul (numeric)],
7: [7 - lump (numeric)],
8: [8 - belt (numeric)],
9: [9 - cranston (numeric)],
10: [10 - sympathi (numeric)],
11... | {'MajorityClassSize': 91.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 6.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 5833.0,
'NumberOfInstances': 204.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5832.0,
'NumberOfSymbolicFeatures': 1.0,
... | tr23.wc | [
"utmost",
"secondli",
"neglect",
"shalala",
"weekend",
"timefram",
"formul",
"lump",
"belt",
"cranston",
"sympathi",
"wrap",
"lectur",
"underfund",
"embargo",
"glad",
"glar",
"multifamili",
"perkin",
"messr",
"demean",
"cop",
"depart",
"multiyear",
"blue",
"romero",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,815 |
4,227 | predictive_accuracy | accuracy_score | tr11.wc | null | {0: [0 - outfit (numeric)],
1: [1 - hasn (numeric)],
2: [2 - calm (numeric)],
3: [3 - gene (numeric)],
4: [4 - resettl (numeric)],
5: [5 - lotteri (numeric)],
6: [6 - privileg (numeric)],
7: [7 - junior (numeric)],
8: [8 - withdrawn (numeric)],
9: [9 - chok (numeric)],
10: [10 - compaq (numeric)],
11: [11 - ... | {'MajorityClassSize': 132.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 6.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 6430.0,
'NumberOfInstances': 414.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6429.0,
'NumberOfSymbolicFeatures': 1.0,... | tr11.wc | [
"outfit",
"hasn",
"calm",
"gene",
"resettl",
"lotteri",
"privileg",
"junior",
"withdrawn",
"chok",
"compaq",
"noi",
"colombia",
"radioact",
"neutron",
"npc",
"disabl",
"macedonian",
"eman",
"skopje",
"volkov",
"appall",
"sidelin",
"titanium",
"quasi",
"league",
"n... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,816 |
3,808 | predictive_accuracy | accuracy_score | kidney | **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 - patient (numeric)],
1: [1 - time (numeric)],
2: [2 - status (nominal)],
3: [3 - age (numeric)],
4: [4 - sex (nominal)],
5: [5 - disease_type (nominal)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 76.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 4.0,
'cost... | kidney | [
"patient",
"time",
"status",
"age",
"sex",
"disease_type"
] | [
false,
false,
true,
false,
true,
true
] | 1,817 |
3,813 | predictive_accuracy | accuracy_score | arsenic-female-lung | **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 - group (nominal)],
1: [1 - conc (numeric)],
2: [2 - age (numeric)],
3: [3 - at.risk (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 540.0,
'MaxNominalAttDistinctValues': 43.0,
'MinorityClassSize': 19.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 559.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | arsenic-female-lung | [
"group",
"conc",
"age",
"at.risk"
] | [
true,
false,
false,
false
] | 1,818 |
3,793 | predictive_accuracy | accuracy_score | colleges_usnews | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - FICE (numeric)],
1: [1 - College_name (nominal)],
2: [2 - State (nominal)],
3: [3 - Public/private_indicator (numeric)],
4: [4 - Average_Math_SAT_score (numeric)],
5: [5 - Average_Verbal_SAT_score (numeric)],
6: [6 - Average_Combined_SAT_score (numeric)],
7: [7 - Average_ACT_score (numeric)],
8: [8 - F... | {'MajorityClassSize': 688.0,
'MaxNominalAttDistinctValues': 51.0,
'MinorityClassSize': 614.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 34.0,
'NumberOfInstances': 1302.0,
'NumberOfInstancesWithMissingValues': 1144.0,
'NumberOfMissingValues': 7830.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures'... | colleges_usnews | [
"FICE",
"State",
"Public/private_indicator",
"Average_Math_SAT_score",
"Average_Verbal_SAT_score",
"Average_Combined_SAT_score",
"Average_ACT_score",
"First_quartile-Math_SAT",
"Third_quartile-Math_SAT",
"First_quartile-Verbal_SAT",
"Third_quartile-Verbal_SAT",
"First_quartile-ACT",
"Third_q... | [
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,819 |
4,240 | predictive_accuracy | accuracy_score | tr41.wc | null | {0: [0 - lighthous (numeric)],
1: [1 - conquest (numeric)],
2: [2 - algerian (numeric)],
3: [3 - banish (numeric)],
4: [4 - jerri (numeric)],
5: [5 - nationalist (numeric)],
6: [6 - evil (numeric)],
7: [7 - beirut (numeric)],
8: [8 - worrisom (numeric)],
9: [9 - stolen (numeric)],
10: [10 - durabl (numeric)],... | {'MajorityClassSize': 243.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 9.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 7455.0,
'NumberOfInstances': 878.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7454.0,
'NumberOfSymbolicFeatures': 1.... | tr41.wc | [
"lighthous",
"conquest",
"algerian",
"banish",
"jerri",
"nationalist",
"evil",
"beirut",
"worrisom",
"stolen",
"durabl",
"adburgham",
"restart",
"lee",
"avonmouth",
"wive",
"exot",
"tighter",
"tenth",
"polic",
"diane",
"perish",
"altimet",
"princeton",
"eb",
"diseas... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,820 |
3,824 | predictive_accuracy | accuracy_score | analcatdata_broadwaymult | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Show (nominal)],
1: [1 - Type (nominal)],
2: [2 - Revival (nominal)],
3: [3 - NYT_rating (numeric)],
4: [4 - DN_rating (numeric)],
5: [5 - Week_1_attendance (numeric)],
6: [6 - Award (nominal)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 167.0,
'MaxNominalAttDistinctValues': 95.0,
'MinorityClassSize': 118.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 285.0,
'NumberOfInstancesWithMissingValues': 18.0,
'NumberOfMissingValues': 27.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
... | analcatdata_broadwaymult | [
"Show",
"Type",
"Revival",
"NYT_rating",
"DN_rating",
"Week_1_attendance",
"Award"
] | [
true,
true,
true,
false,
false,
false,
true
] | 1,821 |
4,223 | predictive_accuracy | accuracy_score | tr45.wc | null | {0: [0 - sunni (numeric)],
1: [1 - interbank (numeric)],
2: [2 - rig (numeric)],
3: [3 - dlouhi (numeric)],
4: [4 - bratislava (numeric)],
5: [5 - librari (numeric)],
6: [6 - number (numeric)],
7: [7 - contamin (numeric)],
8: [8 - mold (numeric)],
9: [9 - norm (numeric)],
10: [10 - mobil (numeric)],
11: [11 ... | {'MajorityClassSize': 160.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 14.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 8262.0,
'NumberOfInstances': 690.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8261.0,
'NumberOfSymbolicFeatures': 1... | tr45.wc | [
"sunni",
"interbank",
"rig",
"dlouhi",
"bratislava",
"librari",
"number",
"contamin",
"mold",
"norm",
"mobil",
"sens",
"plankton",
"ash",
"workstat",
"cabin",
"sunken",
"tengiz",
"cove",
"tragedi",
"alloi",
"fluid",
"escort",
"allot",
"curi",
"tac",
"komsomolet",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,822 |
3,819 | predictive_accuracy | accuracy_score | molecular-biology_promoters | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - class (nominal)],
1: [1 - instance (nominal)],
2: [2 - p-50 (nominal)],
3: [3 - p-49 (nominal)],
4: [4 - p-48 (nominal)],
5: [5 - p-47 (nominal)],
6: [6 - p-46 (nominal)],
7: [7 - p-45 (nominal)],
8: [8 - p-44 (nominal)],
9: [9 - p-43 (nominal)],
10: [10 - p-42 (nominal)],
11: [11 - p-41 (nominal)],... | {'MajorityClassSize': 72.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 34.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 58.0,
'NumberOfInstances': 106.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 58.0,
'c... | molecular-biology_promoters | [
"class",
"p-50",
"p-49",
"p-48",
"p-47",
"p-46",
"p-45",
"p-44",
"p-43",
"p-42",
"p-41",
"p-40",
"p-39",
"p-38",
"p-37",
"p-36",
"p-35",
"p-34",
"p-33",
"p-32",
"p-31",
"p-30",
"p-29",
"p-28",
"p-27",
"p-26",
"p-25",
"p-24",
"p-23",
"p-22",
"p-21",
"p-20... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 1,823 |
3,803 | predictive_accuracy | accuracy_score | water-treatment | **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 - date (nominal)],
1: [1 - Q-E (nominal)],
2: [2 - ZN-E (numeric)],
3: [3 - PH-E (numeric)],
4: [4 - DBO-E (nominal)],
5: [5 - DQO-E (nominal)],
6: [6 - SS-E (nominal)],
7: [7 - SSV-E (numeric)],
8: [8 - SED-E (numeric)],
9: [9 - COND-E (nominal)],
10: [10 - PH-P (numeric)],
11: [11 - DBO-P (nominal)]... | {'MajorityClassSize': 447.0,
'MaxNominalAttDistinctValues': 414.0,
'MinorityClassSize': 80.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 527.0,
'NumberOfInstancesWithMissingValues': 130.0,
'NumberOfMissingValues': 542.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1... | water-treatment | [
"ZN-E",
"PH-E",
"DBO-E",
"DQO-E",
"SS-E",
"SSV-E",
"SED-E",
"COND-E",
"PH-P",
"DBO-P",
"SS-P",
"SSV-P",
"SED-P",
"COND-P",
"PH-D",
"DBO-D",
"DQO-D",
"SS-D",
"SSV-D",
"SED-D",
"COND-D",
"PH-S",
"DBO-S",
"DQO-S",
"SS-S",
"SSV-S",
"SED-S",
"COND-S",
"RD-DBO-P",
... | [
false,
false,
true,
true,
true,
false,
false,
true,
false,
true,
true,
false,
false,
true,
false,
true,
true,
true,
false,
false,
true,
false,
true,
true,
true,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,824 |
3,820 | predictive_accuracy | accuracy_score | braziltourism | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Age (numeric)],
1: [1 - Sex (nominal)],
2: [2 - Income (numeric)],
3: [3 - Travel_cost (numeric)],
4: [4 - Access_road (nominal)],
5: [5 - Active (nominal)],
6: [6 - Passive (nominal)],
7: [7 - Logged_income (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 318.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 94.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 412.0,
'NumberOfInstancesWithMissingValues': 49.0,
'NumberOfMissingValues': 96.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 5.0,
'... | braziltourism | [
"Age",
"Sex",
"Income",
"Travel_cost",
"Access_road",
"Active",
"Passive",
"Logged_income"
] | [
false,
true,
false,
false,
true,
true,
true,
false
] | 1,825 |
3,823 | predictive_accuracy | accuracy_score | postoperative-patient-data | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - L-CORE (nominal)],
1: [1 - L-SURF (nominal)],
2: [2 - L-O2 (nominal)],
3: [3 - L-BP (nominal)],
4: [4 - SURF-STBL (nominal)],
5: [5 - CORE-STBL (nominal)],
6: [6 - BP-STBL (nominal)],
7: [7 - COMFORT (nominal)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 64.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 26.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 90.0,
'NumberOfInstancesWithMissingValues': 3.0,
'NumberOfMissingValues': 3.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 9.0,
'cost... | postoperative-patient-data | [
"L-CORE",
"L-SURF",
"L-O2",
"L-BP",
"SURF-STBL",
"CORE-STBL",
"BP-STBL",
"COMFORT"
] | [
true,
true,
true,
true,
true,
true,
true,
true
] | 1,826 |
4,229 | predictive_accuracy | accuracy_score | fbis.wc | null | {0: [0 - word-0 (numeric)],
1: [1 - word-1 (numeric)],
2: [2 - word-2 (numeric)],
3: [3 - word-3 (numeric)],
4: [4 - word-4 (numeric)],
5: [5 - word-5 (numeric)],
6: [6 - word-6 (numeric)],
7: [7 - word-7 (numeric)],
8: [8 - word-8 (numeric)],
9: [9 - word-9 (numeric)],
10: [10 - word-10 (numeric)],
11: [11 ... | {'MajorityClassSize': 506.0,
'MaxNominalAttDistinctValues': 17.0,
'MinorityClassSize': 38.0,
'NumberOfClasses': 17.0,
'NumberOfFeatures': 2001.0,
'NumberOfInstances': 2463.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2000.0,
'NumberOfSymbolicFeatures': ... | fbis.wc | [
"word-0",
"word-1",
"word-2",
"word-3",
"word-4",
"word-5",
"word-6",
"word-7",
"word-8",
"word-9",
"word-10",
"word-11",
"word-12",
"word-13",
"word-14",
"word-15",
"word-16",
"word-17",
"word-18",
"word-19",
"word-20",
"word-21",
"word-22",
"word-23",
"word-24",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,827 |
3,818 | predictive_accuracy | accuracy_score | tae | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Whether_of_not_the_TA_is_a_native_English_speaker (nominal)],
1: [1 - Course_instructor (numeric)],
2: [2 - Course (numeric)],
3: [3 - Summer_or_regular_semester (nominal)],
4: [4 - Class_size (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 99.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 52.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 151.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 3.0,
'cos... | tae | [
"Whether_of_not_the_TA_is_a_native_English_speaker",
"Course_instructor",
"Course",
"Summer_or_regular_semester",
"Class_size"
] | [
true,
false,
false,
true,
false
] | 1,828 |
3,827 | predictive_accuracy | accuracy_score | pasture | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - fertiliser (nominal)],
1: [1 - slope (numeric)],
2: [2 - aspect-dev-NW (numeric)],
3: [3 - OlsenP (numeric)],
4: [4 - MinN (numeric)],
5: [5 - TS (numeric)],
6: [6 - Ca-Mg (numeric)],
7: [7 - LOM (numeric)],
8: [8 - NFIX-mean (numeric)],
9: [9 - Eworms-main-3 (numeric)],
10: [10 - Eworms-No-species (... | {'MajorityClassSize': 24.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 12.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 36.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | pasture | [
"fertiliser",
"slope",
"aspect-dev-NW",
"OlsenP",
"MinN",
"TS",
"Ca-Mg",
"LOM",
"NFIX-mean",
"Eworms-main-3",
"Eworms-No-species",
"KUnSat",
"OM",
"Air-Perm",
"Porosity",
"HFRG-pct-mean",
"legume-yield",
"OSPP-pct-mean",
"Jan-Mar-mean-TDR",
"Annual-Mean-Runoff",
"root-surface... | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,829 |
4,225 | predictive_accuracy | accuracy_score | tr31.wc | null | {0: [0 - cone (numeric)],
1: [1 - protection (numeric)],
2: [2 - wir (numeric)],
3: [3 - fright (numeric)],
4: [4 - resold (numeric)],
5: [5 - thirsti (numeric)],
6: [6 - isolina (numeric)],
7: [7 - lucho (numeric)],
8: [8 - wari (numeric)],
9: [9 - sermon (numeric)],
10: [10 - unwis (numeric)],
11: [11 - co... | {'MajorityClassSize': 352.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 10129.0,
'NumberOfInstances': 927.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10128.0,
'NumberOfSymbolicFeatures': 1.... | tr31.wc | [
"cone",
"protection",
"wir",
"fright",
"resold",
"thirsti",
"isolina",
"lucho",
"wari",
"sermon",
"unwis",
"commenc",
"optic",
"ah",
"reservoir",
"au",
"uninjur",
"deflat",
"enclav",
"narcodollar",
"delgado",
"jakarta",
"oxapampa",
"yonsei",
"modul",
"labell",
"in... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,830 |
3,831 | predictive_accuracy | accuracy_score | analcatdata_birthday | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Month (nominal)],
1: [1 - Day (nominal)],
2: [2 - Births (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 312.0,
'MaxNominalAttDistinctValues': 31.0,
'MinorityClassSize': 53.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 365.0,
'NumberOfInstancesWithMissingValues': 30.0,
'NumberOfMissingValues': 30.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 3.0,
... | analcatdata_birthday | [
"Month",
"Day",
"Births"
] | [
true,
true,
false
] | 1,831 |
3,826 | predictive_accuracy | accuracy_score | heart-h | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - chest_pain (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 ... | {'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... | heart-h | [
"age",
"sex",
"chest_pain",
"trestbps",
"chol",
"fbs",
"restecg",
"thalach",
"exang",
"oldpeak",
"slope",
"ca",
"thal"
] | [
false,
true,
true,
false,
false,
true,
true,
false,
true,
false,
true,
false,
true
] | 1,832 |
3,828 | predictive_accuracy | accuracy_score | zoo | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - animal (nominal)],
1: [1 - hair (nominal)],
2: [2 - feathers (nominal)],
3: [3 - eggs (nominal)],
4: [4 - milk (nominal)],
5: [5 - airborne (nominal)],
6: [6 - aquatic (nominal)],
7: [7 - predator (nominal)],
8: [8 - toothed (nominal)],
9: [9 - backbone (nominal)],
10: [10 - breathes (nominal)],
11:... | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 41.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 101.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 16.0,
'c... | zoo | [
"hair",
"feathers",
"eggs",
"milk",
"airborne",
"aquatic",
"predator",
"toothed",
"backbone",
"breathes",
"venomous",
"fins",
"legs",
"tail",
"domestic",
"catsize"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true
] | 1,833 |
3,821 | predictive_accuracy | accuracy_score | segment | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - region-centroid-col (numeric)],
1: [1 - region-centroid-row (numeric)],
2: [2 - region-pixel-count (numeric)],
3: [3 - short-line-density-5 (numeric)],
4: [4 - short-line-density-2 (numeric)],
5: [5 - vedge-mean (numeric)],
6: [6 - vegde-sd (numeric)],
7: [7 - hedge-mean (numeric)],
8: [8 - hedge-sd (n... | {'MajorityClassSize': 1980.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 330.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 2310.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 1.0,... | segment | [
"region-centroid-col",
"region-centroid-row",
"region-pixel-count",
"short-line-density-5",
"short-line-density-2",
"vedge-mean",
"vegde-sd",
"hedge-mean",
"hedge-sd",
"intensity-mean",
"rawred-mean",
"rawblue-mean",
"rawgreen-mean",
"exred-mean",
"exblue-mean",
"exgreen-mean",
"valu... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,834 |
3,830 | predictive_accuracy | accuracy_score | cars | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - name (nominal)],
1: [1 - mpg (numeric)],
2: [2 - cylinders (nominal)],
3: [3 - displacement (numeric)],
4: [4 - horsepower (numeric)],
5: [5 - weight (numeric)],
6: [6 - acceleration (numeric)],
7: [7 - model.year (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 254.0,
'MaxNominalAttDistinctValues': 312.0,
'MinorityClassSize': 152.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 406.0,
'NumberOfInstancesWithMissingValues': 14.0,
'NumberOfMissingValues': 14.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 3.0,... | cars | [
"name",
"mpg",
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
"model.year"
] | [
true,
false,
true,
false,
false,
false,
false,
false
] | 1,835 |
3,836 | predictive_accuracy | accuracy_score | wine | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Alcohol (numeric)],
1: [1 - Malic_acid (numeric)],
2: [2 - Ash (numeric)],
3: [3 - Alcalinity_of_ash (numeric)],
4: [4 - Magnesium (numeric)],
5: [5 - Total_phenols (numeric)],
6: [6 - Flavanoids (numeric)],
7: [7 - Nonflavanoid_phenols (numeric)],
8: [8 - Proanthocyanins (numeric)],
9: [9 - Color_int... | {'MajorityClassSize': 107.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 71.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 178.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 1.0,
'... | wine | [
"Alcohol",
"Malic_acid",
"Ash",
"Alcalinity_of_ash",
"Magnesium",
"Total_phenols",
"Flavanoids",
"Nonflavanoid_phenols",
"Proanthocyanins",
"Color_intensity",
"Hue",
"OD280/OD315_of_diluted_wines",
"Proline"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,836 |
3,825 | predictive_accuracy | accuracy_score | mfeat-morphological | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - att1 (numeric)],
1: [1 - att2 (numeric)],
2: [2 - att3 (numeric)],
3: [3 - att4 (numeric)],
4: [4 - att5 (numeric)],
5: [5 - att6 (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 1800.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 200.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | mfeat-morphological | [
"att1",
"att2",
"att3",
"att4",
"att5",
"att6"
] | [
false,
false,
false,
false,
false,
false
] | 1,837 |
3,832 | predictive_accuracy | accuracy_score | iris | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - sepallength (numeric)],
1: [1 - sepalwidth (numeric)],
2: [2 - petallength (numeric)],
3: [3 - petalwidth (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 100.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 50.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 150.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | iris | [
"sepallength",
"sepalwidth",
"petallength",
"petalwidth"
] | [
false,
false,
false,
false
] | 1,838 |
3,838 | predictive_accuracy | accuracy_score | autos | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - normalized-losses (numeric)],
1: [1 - make (nominal)],
2: [2 - fuel-type (nominal)],
3: [3 - aspiration (nominal)],
4: [4 - num-of-doors (nominal)],
5: [5 - body-style (nominal)],
6: [6 - drive-wheels (nominal)],
7: [7 - engine-location (nominal)],
8: [8 - wheel-base (numeric)],
9: [9 - length (numeri... | {'MajorityClassSize': 138.0,
'MaxNominalAttDistinctValues': 22.0,
'MinorityClassSize': 67.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 205.0,
'NumberOfInstancesWithMissingValues': 46.0,
'NumberOfMissingValues': 59.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 11.0... | autos | [
"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",
"compression-rat... | [
false,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
true,
true,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,839 |
3,837 | predictive_accuracy | accuracy_score | hayes-roth | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - hobby (numeric)],
1: [1 - age (numeric)],
2: [2 - educational_level (numeric)],
3: [3 - marital_status (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 81.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 51.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 132.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | hayes-roth | [
"hobby",
"age",
"educational_level",
"marital_status"
] | [
false,
false,
false,
false
] | 1,840 |
3,848 | predictive_accuracy | accuracy_score | squash-unstored | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - site (nominal)],
1: [1 - daf (nominal)],
2: [2 - fruit (nominal)],
3: [3 - weight (numeric)],
4: [4 - pene (numeric)],
5: [5 - solids (numeric)],
6: [6 - brix (numeric)],
7: [7 - a* (numeric)],
8: [8 - egdd (numeric)],
9: [9 - fgdd (numeric)],
10: [10 - groundspot_a* (numeric)],
11: [11 - glucose (n... | {'MajorityClassSize': 28.0,
'MaxNominalAttDistinctValues': 22.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 24.0,
'NumberOfInstances': 52.0,
'NumberOfInstancesWithMissingValues': 9.0,
'NumberOfMissingValues': 39.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 4.0,
'... | squash-unstored | [
"site",
"daf",
"fruit",
"weight",
"pene",
"solids",
"brix",
"a*",
"egdd",
"fgdd",
"groundspot_a*",
"glucose",
"fructose",
"sucrose",
"total",
"glucose+fructose",
"starch",
"sweetness",
"flavour",
"dry/moist",
"fibre",
"heat_input_emerg",
"heat_input_flower"
] | [
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,842 |
3,847 | predictive_accuracy | accuracy_score | analcatdata_draft | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Actual.date (numeric)],
1: [1 - Lottery.pick.1970 (numeric)],
2: [2 - Lottery.pick.1971 (numeric)],
3: [3 - Month (nominal)],
4: [4 - Month.ordered.picks.1970 (nominal)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 334.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 32.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 366.0,
'NumberOfInstancesWithMissingValues': 1.0,
'NumberOfMissingValues': 1.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'c... | analcatdata_draft | [
"Lottery.pick.1970",
"Lottery.pick.1971",
"Month",
"Month.ordered.picks.1970"
] | [
false,
false,
true,
true
] | 1,843 |
3,816 | predictive_accuracy | accuracy_score | splice | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Instance_name (nominal)],
1: [1 - attribute_1 (nominal)],
2: [2 - attribute_2 (nominal)],
3: [3 - attribute_3 (nominal)],
4: [4 - attribute_4 (nominal)],
5: [5 - attribute_5 (nominal)],
6: [6 - attribute_6 (nominal)],
7: [7 - attribute_7 (nominal)],
8: [8 - attribute_8 (nominal)],
9: [9 - attribute_9 ... | {'MajorityClassSize': 1655.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 1535.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 3190.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 61.0... | splice | [
"attribute_1",
"attribute_2",
"attribute_3",
"attribute_4",
"attribute_5",
"attribute_6",
"attribute_7",
"attribute_8",
"attribute_9",
"attribute_10",
"attribute_11",
"attribute_12",
"attribute_13",
"attribute_14",
"attribute_15",
"attribute_16",
"attribute_17",
"attribute_18",
"... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 1,844 |
3,829 | predictive_accuracy | accuracy_score | analcatdata_halloffame | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Player (nominal)],
1: [1 - Number_seasons (numeric)],
2: [2 - Games_played (numeric)],
3: [3 - At_bats (numeric)],
4: [4 - Runs (numeric)],
5: [5 - Hits (numeric)],
6: [6 - Doubles (numeric)],
7: [7 - Triples (numeric)],
8: [8 - Home_runs (numeric)],
9: [9 - RBIs (numeric)],
10: [10 - Walks (numeric)... | {'MajorityClassSize': 1215.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 125.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 1340.0,
'NumberOfInstancesWithMissingValues': 20.0,
'NumberOfMissingValues': 20.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 2.... | analcatdata_halloffame | [
"Number_seasons",
"Games_played",
"At_bats",
"Runs",
"Hits",
"Doubles",
"Triples",
"Home_runs",
"RBIs",
"Walks",
"Strikeouts",
"Batting_average",
"On_base_pct",
"Slugging_pct",
"Fielding_ave",
"Position"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 1,845 |
3,854 | predictive_accuracy | accuracy_score | car | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - buying (nominal)],
1: [1 - maint (nominal)],
2: [2 - doors (nominal)],
3: [3 - persons (nominal)],
4: [4 - lug_boot (nominal)],
5: [5 - safety (nominal)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 1210.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 518.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1728.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
... | car | [
"buying",
"maint",
"doors",
"persons",
"lug_boot",
"safety"
] | [
true,
true,
true,
true,
true,
true
] | 1,846 |
3,855 | predictive_accuracy | accuracy_score | analcatdata_broadway | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Show (nominal)],
1: [1 - Type (nominal)],
2: [2 - Revival (nominal)],
3: [3 - NYT_rating (numeric)],
4: [4 - DN_rating (numeric)],
5: [5 - Tony_awards (nominal)],
6: [6 - Tony_nominations (nominal)],
7: [7 - Week_1_attendance (numeric)],
8: [8 - Show_run (nominal)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 68.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 27.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 95.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 9.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 6.0,
'cost... | analcatdata_broadway | [
"Type",
"Revival",
"NYT_rating",
"DN_rating",
"Tony_awards",
"Tony_nominations",
"Week_1_attendance",
"Show_run"
] | [
true,
true,
false,
false,
true,
true,
false,
true
] | 1,847 |
3,862 | predictive_accuracy | accuracy_score | audiology | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - age_gt_60 (nominal)],
1: [1 - air (nominal)],
2: [2 - airBoneGap (nominal)],
3: [3 - ar_c (nominal)],
4: [4 - ar_u (nominal)],
5: [5 - bone (nominal)],
6: [6 - boneAbnormal (nominal)],
7: [7 - bser (nominal)],
8: [8 - history_buzzing (nominal)],
9: [9 - history_dizziness (nominal)],
10: [10 - history... | {'MajorityClassSize': 169.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 57.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 70.0,
'NumberOfInstances': 226.0,
'NumberOfInstancesWithMissingValues': 222.0,
'NumberOfMissingValues': 317.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 70.0... | audiology | [
"age_gt_60",
"air",
"airBoneGap",
"ar_c",
"ar_u",
"bone",
"boneAbnormal",
"bser",
"history_buzzing",
"history_dizziness",
"history_fluctuating",
"history_fullness",
"history_heredity",
"history_nausea",
"history_noise",
"history_recruitment",
"history_ringing",
"history_roaring",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 1,848 |
3,846 | predictive_accuracy | accuracy_score | cmc | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Wifes_age (numeric)],
1: [1 - Wifes_education (nominal)],
2: [2 - Husbands_education (nominal)],
3: [3 - Number_of_children_ever_born (numeric)],
4: [4 - Wifes_religion (nominal)],
5: [5 - Wifes_now_working? (nominal)],
6: [6 - Husbands_occupation (nominal)],
7: [7 - Standard-of-living_index (nominal)],... | {'MajorityClassSize': 844.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 629.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 8.0,
... | cmc | [
"Wifes_age",
"Wifes_education",
"Husbands_education",
"Number_of_children_ever_born",
"Wifes_religion",
"Wifes_now_working?",
"Husbands_occupation",
"Standard-of-living_index",
"Media_exposure"
] | [
false,
true,
true,
false,
true,
true,
true,
true,
true
] | 1,849 |
3,845 | predictive_accuracy | accuracy_score | heart-c | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - 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': 165.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 138.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 303.0,
'NumberOfInstancesWithMissingValues': 7.0,
'NumberOfMissingValues': 7.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 8.0,
'... | heart-c | [
"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
] | 1,850 |
3,849 | predictive_accuracy | accuracy_score | analcatdata_marketing | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - X1a (nominal)],
1: [1 - X1b (nominal)],
2: [2 - X1c (nominal)],
3: [3 - X1d (nominal)],
4: [4 - X1e (nominal)],
5: [5 - X1f (nominal)],
6: [6 - X1g (nominal)],
7: [7 - X1h (nominal)],
8: [8 - X1i (nominal)],
9: [9 - X1j (nominal)],
10: [10 - X1k (nominal)],
11: [11 - X1l (nominal)],
12: [12 - X1m (... | {'MajorityClassSize': 249.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 115.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 364.0,
'NumberOfInstancesWithMissingValues': 36.0,
'NumberOfMissingValues': 80.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 33.0,... | analcatdata_marketing | [
"X1a",
"X1b",
"X1c",
"X1d",
"X1e",
"X1f",
"X1g",
"X1h",
"X1i",
"X1j",
"X1k",
"X1l",
"X1m",
"X1n",
"X1o",
"X2a",
"X2b",
"X2c",
"X2d",
"X2e",
"X2f",
"X2g",
"X2h",
"X2i",
"X2j",
"X2k",
"X2l",
"X2m",
"X3a",
"X3b",
"X3c",
"X5"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,851 |
3,859 | predictive_accuracy | accuracy_score | prnn_fglass | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - RI (numeric)],
1: [1 - Na (numeric)],
2: [2 - Mg (numeric)],
3: [3 - Al (numeric)],
4: [4 - Si (numeric)],
5: [5 - K (numeric)],
6: [6 - Ca (numeric)],
7: [7 - Ba (numeric)],
8: [8 - Fe (numeric)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 138.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 76.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 214.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | prnn_fglass | [
"RI",
"Na",
"Mg",
"Al",
"Si",
"K",
"Ca",
"Ba",
"Fe"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,852 |
3,861 | predictive_accuracy | accuracy_score | analcatdata_bondrate | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - City (nominal)],
1: [1 - Population (numeric)],
2: [2 - Per_capita_income (numeric)],
3: [3 - Household_income (numeric)],
4: [4 - Discretionary_income (numeric)],
5: [5 - Publics_in_top_10 (nominal)],
6: [6 - Nonprofits_in_top_10 (nominal)],
7: [7 - For_profits_in_top_10 (nominal)],
8: [8 - Utilities_... | {'MajorityClassSize': 33.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 57.0,
'NumberOfInstancesWithMissingValues': 1.0,
'NumberOfMissingValues': 1.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 7.0,
'co... | analcatdata_bondrate | [
"Population",
"Per_capita_income",
"Household_income",
"Discretionary_income",
"Publics_in_top_10",
"Nonprofits_in_top_10",
"For_profits_in_top_10",
"Utilities_in_top_10",
"Region",
"State_capital"
] | [
false,
false,
false,
false,
true,
true,
true,
true,
true,
true
] | 1,853 |
3,850 | predictive_accuracy | accuracy_score | collins | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Text (nominal)],
1: [1 - FirstPerson (numeric)],
2: [2 - InnerThinking (numeric)],
3: [3 - ThinkPositive (numeric)],
4: [4 - ThinkNegative (numeric)],
5: [5 - ThinkAhead (numeric)],
6: [6 - ThinkBack (numeric)],
7: [7 - Reasoning (numeric)],
8: [8 - Share_SocTies (numeric)],
9: [9 - Direct_Activity (n... | {'MajorityClassSize': 420.0,
'MaxNominalAttDistinctValues': 15.0,
'MinorityClassSize': 80.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 3.0,
... | collins | [
"FirstPerson",
"InnerThinking",
"ThinkPositive",
"ThinkNegative",
"ThinkAhead",
"ThinkBack",
"Reasoning",
"Share_SocTies",
"Direct_Activity",
"Interacting",
"Notifying",
"LinearGuidance",
"WordPicture",
"SpaceInterval",
"Motion",
"PastEvents",
"TimeInterval",
"ShiftingEvents",
"T... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
false,
true
] | 1,854 |
3,851 | predictive_accuracy | accuracy_score | fl2000 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - columns (nominal)],
1: [1 - under (numeric)],
2: [2 - over (numeric)],
3: [3 - Bush (numeric)],
4: [4 - Gore (numeric)],
5: [5 - Browne (numeric)],
6: [6 - Nader (numeric)],
7: [7 - Harris (numeric)],
8: [8 - Hagelin (numeric)],
9: [9 - Buchanan (numeric)],
10: [10 - McReynolds (numeric)],
11: [11 -... | {'MajorityClassSize': 41.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 26.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 67.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | fl2000 | [
"columns",
"under",
"over",
"Bush",
"Gore",
"Browne",
"Nader",
"Harris",
"Hagelin",
"Buchanan",
"McReynolds",
"Phillips",
"Moorehead",
"Chote",
"McCarthy"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,855 |
3,817 | predictive_accuracy | accuracy_score | spectrometer | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - LRS-name (nominal)],
1: [1 - ID-type (nominal)],
2: [2 - Right-Ascension (numeric)],
3: [3 - Declination (numeric)],
4: [4 - Scale_Factor (numeric)],
5: [5 - Blue_base_1 (numeric)],
6: [6 - Blue_base_2 (numeric)],
7: [7 - Red_base_1 (numeric)],
8: [8 - Red_base_2 (numeric)],
9: [9 - blue-band-flux_1 (... | {'MajorityClassSize': 476.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 55.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 102.0,
'NumberOfInstances': 531.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 2.0,
... | spectrometer | [
"ID-type",
"Right-Ascension",
"Declination",
"Scale_Factor",
"Blue_base_1",
"Blue_base_2",
"Red_base_1",
"Red_base_2",
"blue-band-flux_1",
"blue-band-flux_2",
"blue-band-flux_3",
"blue-band-flux_4",
"blue-band-flux_5",
"blue-band-flux_6",
"blue-band-flux_7",
"blue-band-flux_8",
"blue... | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
fa... | 1,856 |
3,860 | predictive_accuracy | accuracy_score | balance-scale | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - left-weight (numeric)],
1: [1 - left-distance (numeric)],
2: [2 - right-weight (numeric)],
3: [3 - right-distance (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 337.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 288.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 625.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | balance-scale | [
"left-weight",
"left-distance",
"right-weight",
"right-distance"
] | [
false,
false,
false,
false
] | 1,857 |
3,864 | predictive_accuracy | accuracy_score | sponge | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Name (nominal)],
1: [1 - A.CAPAS_DEL_CORTEX (nominal)],
2: [2 - A.CAPA_INTERNA_DEL_CORTEX (nominal)],
3: [3 - A.CORTEX (nominal)],
4: [4 - A.CORTEX_FIBROSO (nominal)],
5: [5 - A.CORTEX_SOLO_DE_ESPICULAS_TANGENCIALES (nominal)],
6: [6 - A.CUERPOS_EXTRANOS_EN_EL_CORTEX (nominal)],
7: [7 - A.GROSOR_DEL_COR... | {'MajorityClassSize': 70.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 6.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 45.0,
'NumberOfInstances': 76.0,
'NumberOfInstancesWithMissingValues': 22.0,
'NumberOfMissingValues': 22.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 45.0,
'c... | sponge | [
"A.CAPAS_DEL_CORTEX",
"A.CAPA_INTERNA_DEL_CORTEX",
"A.CORTEX",
"A.CORTEX_FIBROSO",
"A.CORTEX_SOLO_DE_ESPICULAS_TANGENCIALES",
"A.CUERPOS_EXTRANOS_EN_EL_CORTEX",
"A.GROSOR_DEL_CORTEX",
"A.HACES_DE_ESPICULAS_PRINCIPALES_EN_POMPON_EN_EL_CORTEX",
"A.TILOSTILOS_ADICIONALES_COANOSOMA",
"B.NUMERO_DE_TIPO... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 1,858 |
3,866 | predictive_accuracy | accuracy_score | primary-tumor | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - age (nominal)],
1: [1 - sex (nominal)],
2: [2 - histologic-type (nominal)],
3: [3 - degree-of-diffe (nominal)],
4: [4 - bone (nominal)],
5: [5 - bone-marrow (nominal)],
6: [6 - lung (nominal)],
7: [7 - pleura (nominal)],
8: [8 - peritoneum (nominal)],
9: [9 - liver (nominal)],
10: [10 - brain (nomina... | {'MajorityClassSize': 255.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 84.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 18.0,
'NumberOfInstances': 339.0,
'NumberOfInstancesWithMissingValues': 207.0,
'NumberOfMissingValues': 225.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 18.0... | primary-tumor | [
"age",
"sex",
"histologic-type",
"degree-of-diffe",
"bone",
"bone-marrow",
"lung",
"pleura",
"peritoneum",
"liver",
"brain",
"skin",
"neck",
"supraclavicular",
"axillar",
"mediastinum",
"abdominal"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,859 |
3,874 | predictive_accuracy | accuracy_score | ecoli | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - mcg (numeric)],
1: [1 - gvh (numeric)],
2: [2 - lip (numeric)],
3: [3 - chg (numeric)],
4: [4 - aac (numeric)],
5: [5 - alm1 (numeric)],
6: [6 - alm2 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 193.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 143.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 336.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | ecoli | [
"mcg",
"gvh",
"lip",
"chg",
"aac",
"alm1",
"alm2"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,860 |
3,853 | predictive_accuracy | accuracy_score | eucalyptus | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Abbrev (nominal)],
1: [1 - Rep (numeric)],
2: [2 - Locality (nominal)],
3: [3 - Map_Ref (nominal)],
4: [4 - Latitude (nominal)],
5: [5 - Altitude (numeric)],
6: [6 - Rainfall (numeric)],
7: [7 - Frosts (numeric)],
8: [8 - Year (numeric)],
9: [9 - Sp (nominal)],
10: [10 - PMCno (numeric)],
11: [11 - ... | {'MajorityClassSize': 522.0,
'MaxNominalAttDistinctValues': 27.0,
'MinorityClassSize': 214.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 736.0,
'NumberOfInstancesWithMissingValues': 95.0,
'NumberOfMissingValues': 448.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 6.... | eucalyptus | [
"Abbrev",
"Rep",
"Locality",
"Map_Ref",
"Latitude",
"Altitude",
"Rainfall",
"Frosts",
"Year",
"Sp",
"PMCno",
"DBH",
"Ht",
"Surv",
"Vig",
"Ins_res",
"Stem_Fm",
"Crown_Fm",
"Brnch_Fm"
] | [
true,
false,
true,
true,
true,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,861 |
4,236 | predictive_accuracy | accuracy_score | la1s.wc | null | {0: [0 - aa (numeric)],
1: [1 - aaron (numeric)],
2: [2 - ab (numeric)],
3: [3 - aback (numeric)],
4: [4 - abandon (numeric)],
5: [5 - abat (numeric)],
6: [6 - abbe (numeric)],
7: [7 - abbrevi (numeric)],
8: [8 - abc (numeric)],
9: [9 - abdel (numeric)],
10: [10 - abdi (numeric)],
11: [11 - abdomen (numeric)... | {'MajorityClassSize': 943.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 273.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 13196.0,
'NumberOfInstances': 3204.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13195.0,
'NumberOfSymbolicFeatures':... | la1s.wc | [
"aa",
"aaron",
"ab",
"aback",
"abandon",
"abat",
"abbe",
"abbrevi",
"abc",
"abdel",
"abdi",
"abdomen",
"abdomin",
"abduct",
"abdul",
"abdullah",
"abe",
"aberr",
"abet",
"abid",
"abil",
"ablaz",
"able",
"abnorm",
"aboard",
"abolish",
"abort",
"abound",
"abram",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,862 |
3,857 | predictive_accuracy | accuracy_score | vehicle | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - COMPACTNESS (numeric)],
1: [1 - CIRCULARITY (numeric)],
2: [2 - DISTANCE CIRCULARITY (numeric)],
3: [3 - RADIUS RATIO (numeric)],
4: [4 - PR.AXIS ASPECT RATIO (numeric)],
5: [5 - MAX.LENGTH ASPECT RATIO (numeric)],
6: [6 - SCATTER RATIO (numeric)],
7: [7 - ELONGATEDNESS (numeric)],
8: [8 - PR.AXIS RECT... | {'MajorityClassSize': 628.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 218.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 846.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 1.0,
... | vehicle | [
"COMPACTNESS",
"CIRCULARITY",
"DISTANCE CIRCULARITY",
"RADIUS RATIO",
"PR.AXIS ASPECT RATIO",
"MAX.LENGTH ASPECT RATIO",
"SCATTER RATIO",
"ELONGATEDNESS",
"PR.AXIS RECTANGULARITY",
"MAX.LENGTH RECTANGULARITY",
"SCALED VARIANCE_MAJOR",
"SCALED VARIANCE_MINOR",
"SCALED RADIUS OF GYRATION",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,864 |
3,869 | predictive_accuracy | accuracy_score | lymph | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - lymphatics (nominal)],
1: [1 - block_of_affere (nominal)],
2: [2 - bl_of_lymph_c (nominal)],
3: [3 - bl_of_lymph_s (nominal)],
4: [4 - by_pass (nominal)],
5: [5 - extravasates (nominal)],
6: [6 - regeneration_of (nominal)],
7: [7 - early_uptake_in (nominal)],
8: [8 - lym_nodes_dimin (numeric)],
9: [9 ... | {'MajorityClassSize': 81.0,
'MaxNominalAttDistinctValues': 8.0,
'MinorityClassSize': 67.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 148.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 16.0,
'c... | lymph | [
"lymphatics",
"block_of_affere",
"bl_of_lymph_c",
"bl_of_lymph_s",
"by_pass",
"extravasates",
"regeneration_of",
"early_uptake_in",
"lym_nodes_dimin",
"lym_nodes_enlar",
"changes_in_lym",
"defect_in_node",
"changes_in_node",
"changes_in_stru",
"special_forms",
"dislocation_of",
"excl... | [
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
false
] | 1,865 |
3,873 | predictive_accuracy | accuracy_score | dermatology | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - erythema (nominal)],
1: [1 - scaling (nominal)],
2: [2 - definite_borders (nominal)],
3: [3 - itching (nominal)],
4: [4 - koebner_phenomenon (nominal)],
5: [5 - polygonal_papules (nominal)],
6: [6 - follicular_papules (nominal)],
7: [7 - oral_mucosal_involvement (nominal)],
8: [8 - knee_and_elbow_invol... | {'MajorityClassSize': 254.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 112.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 35.0,
'NumberOfInstances': 366.0,
'NumberOfInstancesWithMissingValues': 8.0,
'NumberOfMissingValues': 8.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 34.0,
... | dermatology | [
"erythema",
"scaling",
"definite_borders",
"itching",
"koebner_phenomenon",
"polygonal_papules",
"follicular_papules",
"oral_mucosal_involvement",
"knee_and_elbow_involvement",
"scalp_involvement",
"family_history",
"melanin_incontinence",
"eosinophils_in_the_infiltrate",
"PNL_infiltrate",... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false
] | 1,866 |
3,871 | predictive_accuracy | accuracy_score | analcatdata_reviewer | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Film (nominal)],
1: [1 - Roger_Ebert (nominal)],
2: [2 - Jeffrey_Lyons (nominal)],
3: [3 - Michael_Medved (nominal)],
4: [4 - Rex_Reed (nominal)],
5: [5 - Gene_Shalit (nominal)],
6: [6 - Joel_Siegel (nominal)],
7: [7 - Gene_Siskel (nominal)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 216.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 163.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 379.0,
'NumberOfInstancesWithMissingValues': 367.0,
'NumberOfMissingValues': 1368.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 8.0... | analcatdata_reviewer | [
"Roger_Ebert",
"Jeffrey_Lyons",
"Michael_Medved",
"Rex_Reed",
"Gene_Shalit",
"Joel_Siegel",
"Gene_Siskel"
] | [
true,
true,
true,
true,
true,
true,
true
] | 1,867 |
3,876 | predictive_accuracy | accuracy_score | analcatdata_challenger | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Temperature (numeric)],
1: [1 - Pressure (nominal)],
2: [2 - Damaged (nominal)]} | {'MajorityClassSize': 129.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 9.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 138.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | analcatdata_challenger | [
"Temperature",
"Pressure"
] | [
false,
true
] | 1,868 |
3,875 | predictive_accuracy | accuracy_score | flags | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - name (nominal)],
1: [1 - landmass (nominal)],
2: [2 - zone (nominal)],
3: [3 - area (numeric)],
4: [4 - population (numeric)],
5: [5 - language (nominal)],
6: [6 - religion (nominal)],
7: [7 - bars (nominal)],
8: [8 - stripes (nominal)],
9: [9 - colours (nominal)],
10: [10 - red (nominal)],
11: [11 ... | {'MajorityClassSize': 125.0,
'MaxNominalAttDistinctValues': 14.0,
'MinorityClassSize': 69.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 29.0,
'NumberOfInstances': 194.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 27.0,
... | flags | [
"landmass",
"zone",
"area",
"population",
"language",
"religion",
"bars",
"stripes",
"colours",
"red",
"green",
"blue",
"gold",
"white",
"black",
"orange",
"mainhue",
"circles",
"crosses",
"saltires",
"quarters",
"sunstars",
"crescent",
"triangle",
"icon",
"animate"... | [
true,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,869 |
3,868 | predictive_accuracy | accuracy_score | glass | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - RI (numeric)],
1: [1 - Na (numeric)],
2: [2 - Mg (numeric)],
3: [3 - Al (numeric)],
4: [4 - Si (numeric)],
5: [5 - K (numeric)],
6: [6 - Ca (numeric)],
7: [7 - Ba (numeric)],
8: [8 - Fe (numeric)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 138.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 76.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 214.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | glass | [
"RI",
"Na",
"Mg",
"Al",
"Si",
"K",
"Ca",
"Ba",
"Fe"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,870 |
3,877 | predictive_accuracy | accuracy_score | analcatdata_dmft | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - DMFT.Begin (nominal)],
1: [1 - DMFT.End (nominal)],
2: [2 - Gender (nominal)],
3: [3 - Ethnic (nominal)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 642.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 155.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 797.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 5.0,
'c... | analcatdata_dmft | [
"DMFT.Begin",
"DMFT.End",
"Gender",
"Ethnic"
] | [
true,
true,
true,
true
] | 1,871 |
4,233 | predictive_accuracy | accuracy_score | la2s.wc | null | {0: [0 - aa (numeric)],
1: [1 - aaa (numeric)],
2: [2 - aaron (numeric)],
3: [3 - aase (numeric)],
4: [4 - ab (numeric)],
5: [5 - abandon (numeric)],
6: [6 - abat (numeric)],
7: [7 - abbe (numeric)],
8: [8 - abc (numeric)],
9: [9 - abdi (numeric)],
10: [10 - abdomen (numeric)],
11: [11 - abdomin (numeric)],
... | {'MajorityClassSize': 905.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 248.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12433.0,
'NumberOfInstances': 3075.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12432.0,
'NumberOfSymbolicFeatures':... | la2s.wc | [
"aa",
"aaa",
"aaron",
"aase",
"ab",
"abandon",
"abat",
"abbe",
"abc",
"abdi",
"abdomen",
"abdomin",
"abduct",
"abdul",
"abe",
"abet",
"abhorr",
"abid",
"abil",
"able",
"abnorm",
"aboard",
"abolish",
"abolit",
"abort",
"abound",
"abraham",
"abram",
"abroad",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,872 |
3,887 | predictive_accuracy | accuracy_score | analcatdata_germangss | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - Political_system (nominal)],
1: [1 - Age (nominal)],
2: [2 - Time_of_survey (nominal)],
3: [3 - Schooling (nominal)],
4: [4 - Region (nominal)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 310.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 90.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 6.0,
'co... | analcatdata_germangss | [
"Political_system",
"Age",
"Time_of_survey",
"Schooling",
"Region"
] | [
true,
true,
true,
true,
true
] | 1,873 |
3,839 | predictive_accuracy | accuracy_score | JapaneseVowels | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - utterance (numeric)],
1: [1 - frame (numeric)],
2: [2 - coefficient1 (numeric)],
3: [3 - coefficient2 (numeric)],
4: [4 - coefficient3 (numeric)],
5: [5 - coefficient4 (numeric)],
6: [6 - coefficient5 (numeric)],
7: [7 - coefficient6 (numeric)],
8: [8 - coefficient7 (numeric)],
9: [9 - coefficient8 (n... | {'MajorityClassSize': 8347.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 1614.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 9961.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 1.0... | JapaneseVowels | [
"utterance",
"frame",
"coefficient1",
"coefficient2",
"coefficient3",
"coefficient4",
"coefficient5",
"coefficient6",
"coefficient7",
"coefficient8",
"coefficient9",
"coefficient10",
"coefficient11",
"coefficient12"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,874 |
189,763 | predictive_accuracy | accuracy_score | slashdot | Multi-label dataset for text-classification. It consists of
article titles and partial blurbs. Blurbs can be assigned to several
categories (e.g. Science, News, Games) based on word predictors. | {0: [0 - Entertainment (nominal)],
1: [1 - Interviews (nominal)],
2: [2 - Main (nominal)],
3: [3 - Developers (nominal)],
4: [4 - Apache (nominal)],
5: [5 - News (nominal)],
6: [6 - Search (nominal)],
7: [7 - Mobile (nominal)],
8: [8 - Science (nominal)],
9: [9 - IT (nominal)],
10: [10 - BSD (nominal)],
11: ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 1101.0,
'NumberOfInstances': 3782.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1079.0,
'NumberOfSymbolicFeatures': 22.0,... | slashdot | [
"Entertainment",
"Interviews",
"Main",
"Developers",
"Apache",
"News",
"Search",
"Mobile",
"Science",
"IT",
"BSD",
"Idle",
"Games",
"YourRightsOnline",
"AskSlashdot",
"Apple",
"BookReviews",
"Hardware",
"Meta",
"Linux",
"Politics",
"Technology",
"X0",
"X000",
"X1",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false... | 1,875 |
3,842 | predictive_accuracy | accuracy_score | waveform-5000 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - x1 (numeric)],
1: [1 - x2 (numeric)],
2: [2 - x3 (numeric)],
3: [3 - x4 (numeric)],
4: [4 - x5 (numeric)],
5: [5 - x6 (numeric)],
6: [6 - x7 (numeric)],
7: [7 - x8 (numeric)],
8: [8 - x9 (numeric)],
9: [9 - x10 (numeric)],
10: [10 - x11 (numeric)],
11: [11 - x12 (numeric)],
12: [12 - x13 (numeric)]... | {'MajorityClassSize': 3308.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 1692.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 41.0,
'NumberOfInstances': 5000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 40.0,
'NumberOfSymbolicFeatures': 1.0... | waveform-5000 | [
"x1",
"x2",
"x3",
"x4",
"x5",
"x6",
"x7",
"x8",
"x9",
"x10",
"x11",
"x12",
"x13",
"x14",
"x15",
"x16",
"x17",
"x18",
"x19",
"x20",
"x21",
"x22",
"x23",
"x24",
"x25",
"x26",
"x27",
"x28",
"x29",
"x30",
"x31",
"x32",
"x33",
"x34",
"x35",
"x36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,876 |
3,908 | predictive_accuracy | accuracy_score | usp05-ft | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This is a PROMISE Software Engineering Repository data set made publicly
available in order to encourage repeatable, verifiable, refutable, and/or
improvable predictive mod... | {0: [0 - ID (numeric)],
1: [1 - Effort (numeric)],
2: [2 - IntComplx (numeric)],
3: [3 - DataFile (numeric)],
4: [4 - DataEn (numeric)],
5: [5 - DataOut (numeric)],
6: [6 - UFP (numeric)],
7: [7 - Lang (nominal)],
8: [8 - Tools (nominal)],
9: [9 - ToolExpr (nominal)],
10: [10 - AppExpr (numeric)],
11: [11 - ... | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 16.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 76.0,
'NumberOfInstancesWithMissingValues': 18.0,
'NumberOfMissingValues': 37.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | usp05-ft | [
"ID",
"Effort",
"IntComplx",
"DataFile",
"DataEn",
"DataOut",
"UFP",
"Lang",
"Tools",
"ToolExpr",
"AppExpr",
"TeamSize",
"DBMS",
"Method"
] | [
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
false,
true,
true,
true
] | 1,877 |
3,872 | predictive_accuracy | accuracy_score | white-clover | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - strata (nominal)],
1: [1 - plot (nominal)],
2: [2 - paddock (nominal)],
3: [3 - WhiteClover-91 (numeric)],
4: [4 - BareGround-91 (numeric)],
5: [5 - Cocksfoot-91 (numeric)],
6: [6 - OtherGrasses-91 (numeric)],
7: [7 - OtherLegumes-91 (numeric)],
8: [8 - RyeGrass-91 (numeric)],
9: [9 - Weeds-91 (numeri... | {'MajorityClassSize': 38.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 25.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 32.0,
'NumberOfInstances': 63.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 5.0,
'co... | white-clover | [
"strata",
"plot",
"paddock",
"WhiteClover-91",
"BareGround-91",
"Cocksfoot-91",
"OtherGrasses-91",
"OtherLegumes-91",
"RyeGrass-91",
"Weeds-91",
"WhiteClover-92",
"BareGround-92",
"Cocksfoot-92",
"OtherGrasses-92",
"OtherLegumes-92",
"RyeGrass-92",
"Weeds-92",
"WhiteClover-93",
"... | [
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 1,878 |
3,878 | predictive_accuracy | accuracy_score | confidence | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - P (numeric)],
1: [1 - N (numeric)],
2: [2 - O (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 12.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 72.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | confidence | [
"P",
"N",
"O"
] | [
false,
false,
false
] | 1,879 |
3,905 | predictive_accuracy | accuracy_score | mc2 | **Author**: Mike Chapman, NASA
**Source**: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/mc2.html) - 2004
**Please cite**: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, C... | {0: [0 - LOC_BLANK (numeric)],
1: [1 - BRANCH_COUNT (numeric)],
2: [2 - CALL_PAIRS (numeric)],
3: [3 - LOC_CODE_AND_COMMENT (numeric)],
4: [4 - LOC_COMMENTS (numeric)],
5: [5 - CONDITION_COUNT (numeric)],
6: [6 - CYCLOMATIC_COMPLEXITY (numeric)],
7: [7 - CYCLOMATIC_DENSITY (numeric)],
8: [8 - DECISION_COUNT (nu... | {'MajorityClassSize': 109.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 52.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 40.0,
'NumberOfInstances': 161.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 39.0,
'NumberOfSymbolicFeatures': 1.0,
'... | mc2 | [
"LOC_BLANK",
"BRANCH_COUNT",
"CALL_PAIRS",
"LOC_CODE_AND_COMMENT",
"LOC_COMMENTS",
"CONDITION_COUNT",
"CYCLOMATIC_COMPLEXITY",
"CYCLOMATIC_DENSITY",
"DECISION_COUNT",
"DECISION_DENSITY",
"DESIGN_COMPLEXITY",
"DESIGN_DENSITY",
"EDGE_COUNT",
"ESSENTIAL_COMPLEXITY",
"ESSENTIAL_DENSITY",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 1,881 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.