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 |
|---|---|---|---|---|---|---|---|---|---|---|
4,492 | predictive_accuracy | accuracy_score | humandevel | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - rank (numeric)],
1: [1 - country (nominal)],
2: [2 - hdi (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 65.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 65.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | humandevel | [
"hdi"
] | [
false
] | 2,198 |
4,489 | predictive_accuracy | accuracy_score | analcatdata_seropositive | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Age (numeric)],
1: [1 - Disease (nominal)],
2: [2 - Positive (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 86.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 46.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 132.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | analcatdata_seropositive | [
"Age",
"Disease",
"Positive"
] | [
false,
true,
false
] | 2,199 |
4,490 | 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... | 2,200 |
4,494 | 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
] | 2,201 |
4,497 | 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
] | 2,202 |
4,496 | 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
] | 2,203 |
4,502 | 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
] | 2,204 |
4,500 | 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... | 2,205 |
4,491 | 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
] | 2,206 |
4,501 | 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
] | 2,207 |
4,485 | 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
] | 2,208 |
4,507 | 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
] | 2,209 |
4,499 | 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
] | 2,210 |
3,941 | predictive_accuracy | accuracy_score | GCM | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Multiclass cancer diagnosis using 16063 tumor gene expression signatures.
PNAS, VOL 98, no 26, pp. 15149-15154, December 18, 2001.
S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C.-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J.P. Mes... | {0: [0 - AFFX-BioB-5_at (numeric)],
1: [1 - AFFX-BioB-M_at (numeric)],
2: [2 - AFFX-BioB-3_at (numeric)],
3: [3 - AFFX-BioC-5_at (numeric)],
4: [4 - AFFX-BioC-3_at (numeric)],
5: [5 - AFFX-BioDn-5_at (numeric)],
6: [6 - AFFX-BioDn-3_at (numeric)],
7: [7 - AFFX-CreX-5_at (numeric)],
8: [8 - AFFX-CreX-3_at (numer... | {'MajorityClassSize': 30.0,
'MaxNominalAttDistinctValues': 14.0,
'MinorityClassSize': 10.0,
'NumberOfClasses': 14.0,
'NumberOfFeatures': 16064.0,
'NumberOfInstances': 190.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16063.0,
'NumberOfSymbolicFeatures': ... | GCM | [
"AFFX-BioB-5_at",
"AFFX-BioB-M_at",
"AFFX-BioB-3_at",
"AFFX-BioC-5_at",
"AFFX-BioC-3_at",
"AFFX-BioDn-5_at",
"AFFX-BioDn-3_at",
"AFFX-CreX-5_at",
"AFFX-CreX-3_at",
"AFFX-BioB-5_st",
"AFFX-BioB-M_st",
"AFFX-BioB-3_st",
"AFFX-BioC-5_st",
"AFFX-BioC-3_st",
"AFFX-BioDn-5_st",
"AFFX-BioDn-3... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,211 |
3,989 | predictive_accuracy | accuracy_score | AP_Prostate_Lung | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
1: [1 - 1007_s_at (numeric)],
2: [2 - 117_at (numeric)],
3: [3 - 121_at (numeric)],
4: [4 - 1405_i_at (numeric)],
5: [5 - 1552257_a_at (numeric)],
6: [6 - 1552283_s_at (numeric)],
7: [7 - 1552309_a_at (numeric)],
8: [8 - 1552348_at (numeric)],
9: [9 - 1552365_at (numeric)],
10: [10... | {'MajorityClassSize': 126.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 69.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 195.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Prostate_Lung | [
"1007_s_at",
"117_at",
"121_at",
"1405_i_at",
"1552257_a_at",
"1552283_s_at",
"1552309_a_at",
"1552348_at",
"1552365_at",
"1552368_at",
"1552426_a_at",
"1552455_at",
"1552463_at",
"1552477_a_at",
"1552610_a_at",
"1552615_at",
"1552619_a_at",
"1552621_at",
"1552622_s_at",
"15526... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,212 |
4,503 | 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
] | 2,213 |
4,472 | 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... | 2,214 |
4,504 | 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
] | 2,215 |
4,506 | 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
] | 2,216 |
4,505 | 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... | 2,217 |
4,510 | 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
] | 2,218 |
4,511 | 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
] | 2,219 |
4,509 | 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
] | 2,220 |
4,514 | 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
] | 2,221 |
4,512 | 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
] | 2,222 |
4,519 | 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
] | 2,223 |
3,970 | predictive_accuracy | accuracy_score | AP_Lung_Uterus | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
1: [1 - 1007_s_at (numeric)],
2: [2 - 121_at (numeric)],
3: [3 - 1405_i_at (numeric)],
4: [4 - 1438_at (numeric)],
5: [5 - 1552257_a_at (numeric)],
6: [6 - 1552283_s_at (numeric)],
7: [7 - 1552309_a_at (numeric)],
8: [8 - 1552348_at (numeric)],
9: [9 - 1552365_at (numeric)],
10: [1... | {'MajorityClassSize': 126.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 124.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': ... | AP_Lung_Uterus | [
"1007_s_at",
"121_at",
"1405_i_at",
"1438_at",
"1552257_a_at",
"1552283_s_at",
"1552309_a_at",
"1552348_at",
"1552365_at",
"1552368_at",
"1552378_s_at",
"1552426_a_at",
"1552477_a_at",
"1552610_a_at",
"1552615_at",
"1552619_a_at",
"1552621_at",
"1552622_s_at",
"1552626_a_at",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,224 |
4,517 | 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
] | 2,225 |
4,515 | 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
] | 2,226 |
4,513 | 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
] | 2,227 |
4,520 | 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
] | 2,228 |
4,518 | 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
] | 2,229 |
4,523 | 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
] | 2,230 |
4,525 | 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
] | 2,231 |
4,529 | 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
] | 2,232 |
4,533 | 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
] | 2,233 |
4,535 | 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
] | 2,234 |
4,528 | 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
] | 2,235 |
4,530 | 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
] | 2,236 |
4,498 | 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
] | 2,237 |
4,536 | 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
] | 2,238 |
4,532 | 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
] | 2,240 |
4,531 | 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
] | 2,241 |
3,926 | predictive_accuracy | accuracy_score | rsctc2010_3 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data from the RSCTC 2010 Discovery Challenge. Example datasets for 6 different problems of DNA microarray data analysis and classification. All datasets contain gene expression data characterized by values of 20,000 - 65,000 attributes. Samples ar... | {0: [0 - Var1 (numeric)],
1: [1 - Var2 (numeric)],
2: [2 - Var3 (numeric)],
3: [3 - Var4 (numeric)],
4: [4 - Var5 (numeric)],
5: [5 - Var6 (numeric)],
6: [6 - Var7 (numeric)],
7: [7 - Var8 (numeric)],
8: [8 - Var9 (numeric)],
9: [9 - Var10 (numeric)],
10: [10 - Var11 (numeric)],
11: [11 - Var12 (numeric)],
... | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 5.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 22278.0,
'NumberOfInstances': 95.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22277.0,
'NumberOfSymbolicFeatures': 1.0,... | rsctc2010_3 | [
"Var1",
"Var2",
"Var3",
"Var4",
"Var5",
"Var6",
"Var7",
"Var8",
"Var9",
"Var10",
"Var11",
"Var12",
"Var13",
"Var14",
"Var15",
"Var16",
"Var17",
"Var18",
"Var19",
"Var20",
"Var21",
"Var22",
"Var23",
"Var24",
"Var25",
"Var26",
"Var27",
"Var28",
"Var29",
"Var30... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,242 |
4,508 | 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
] | 2,243 |
4,542 | 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
] | 2,244 |
4,537 | 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
] | 2,245 |
4,541 | 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
] | 2,246 |
4,526 | 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
] | 2,247 |
4,543 | 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
] | 2,248 |
4,534 | 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
] | 2,249 |
4,554 | 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
] | 2,251 |
4,552 | 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
] | 2,252 |
4,521 | 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... | 2,253 |
4,551 | 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
] | 2,254 |
4,559 | 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
] | 2,255 |
4,553 | 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
] | 2,256 |
4,550 | 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
] | 2,257 |
4,556 | 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
] | 2,258 |
4,560 | 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
] | 2,259 |
4,566 | 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
] | 2,260 |
4,569 | 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... | 2,261 |
4,564 | 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
] | 2,262 |
4,567 | 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... | 2,263 |
4,522 | 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... | 2,264 |
4,190 | predictive_accuracy | accuracy_score | scene | **Author**: Matthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown.
**Source**: [Mulan](http://mulan.sourceforge.net/datasets-mlc.html)
**Please cite**:
### Description
Scene recognition dataset - It contains characteristics about images and their classes.
The original dataset is a multi-label ... | {0: [0 - attr1 (numeric)],
1: [1 - attr2 (numeric)],
2: [2 - attr3 (numeric)],
3: [3 - attr4 (numeric)],
4: [4 - attr5 (numeric)],
5: [5 - attr6 (numeric)],
6: [6 - attr7 (numeric)],
7: [7 - attr8 (numeric)],
8: [8 - attr9 (numeric)],
9: [9 - attr10 (numeric)],
10: [10 - attr11 (numeric)],
11: [11 - attr12 (... | {'MajorityClassSize': 1976.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 431.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 300.0,
'NumberOfInstances': 2407.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 294.0,
'NumberOfSymbolicFeatures': 6.... | scene | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6",
"attr7",
"attr8",
"attr9",
"attr10",
"attr11",
"attr12",
"attr13",
"attr14",
"attr15",
"attr16",
"attr17",
"attr18",
"attr19",
"attr20",
"attr21",
"attr22",
"attr23",
"attr24",
"attr25",
"attr26",
"attr27",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,265 |
4,565 | 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
] | 2,266 |
3,990 | predictive_accuracy | accuracy_score | AP_Omentum_Ovary | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
1: [1 - 1007_s_at (numeric)],
2: [2 - 121_at (numeric)],
3: [3 - 1405_i_at (numeric)],
4: [4 - 1552256_a_at (numeric)],
5: [5 - 1552257_a_at (numeric)],
6: [6 - 1552261_at (numeric)],
7: [7 - 1552348_at (numeric)],
8: [8 - 1552349_a_at (numeric)],
9: [9 - 1552365_at (numeric)],
10:... | {'MajorityClassSize': 198.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 77.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 275.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Omentum_Ovary | [
"1007_s_at",
"121_at",
"1405_i_at",
"1552256_a_at",
"1552257_a_at",
"1552261_at",
"1552348_at",
"1552349_a_at",
"1552365_at",
"1552368_at",
"1552426_a_at",
"1552456_a_at",
"1552477_a_at",
"1552566_at",
"1552594_at",
"1552610_a_at",
"1552619_a_at",
"1552621_at",
"1552622_s_at",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,267 |
4,578 | 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
] | 2,268 |
4,577 | 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
] | 2,269 |
4,562 | 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
] | 2,270 |
4,574 | 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
] | 2,271 |
4,579 | 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
] | 2,273 |
4,558 | 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
] | 2,274 |
4,547 | 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... | 2,275 |
4,571 | 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
] | 2,276 |
4,544 | 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
] | 2,277 |
4,581 | 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
] | 2,278 |
4,580 | 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
] | 2,279 |
4,576 | 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
] | 2,280 |
4,583 | 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
] | 2,281 |
4,538 | predictive_accuracy | accuracy_score | analcatdata_authorship | **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 - a (numeric)],
1: [1 - all (numeric)],
2: [2 - also (numeric)],
3: [3 - an (numeric)],
4: [4 - and (numeric)],
5: [5 - any (numeric)],
6: [6 - are (numeric)],
7: [7 - as (numeric)],
8: [8 - at (numeric)],
9: [9 - be (numeric)],
10: [10 - been (numeric)],
11: [11 - but (numeric)],
12: [12 - by (numer... | {'MajorityClassSize': 524.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 317.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 71.0,
'NumberOfInstances': 841.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 70.0,
'NumberOfSymbolicFeatures': 1.0,
... | analcatdata_authorship | [
"a",
"all",
"also",
"an",
"and",
"any",
"are",
"as",
"at",
"be",
"been",
"but",
"by",
"can",
"do",
"down",
"even",
"every",
"for",
"from",
"had",
"has",
"have",
"her",
"his",
"if",
"in",
"into",
"is",
"it",
"its",
"may",
"more",
"must",
"my",
"no... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,282 |
4,573 | 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
] | 2,283 |
4,557 | predictive_accuracy | accuracy_score | anneal | **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 - family (nominal)],
1: [1 - product-type (nominal)],
2: [2 - steel (nominal)],
3: [3 - carbon (numeric)],
4: [4 - hardness (numeric)],
5: [5 - temper_rolling (nominal)],
6: [6 - condition (nominal)],
7: [7 - formability (nominal)],
8: [8 - strength (numeric)],
9: [9 - non-ageing (nominal)],
10: [10 - ... | {'MajorityClassSize': 684.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 214.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 898.0,
'NumberOfMissingValues': 22175.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 3... | anneal | [
"family",
"product-type",
"steel",
"carbon",
"hardness",
"temper_rolling",
"condition",
"formability",
"strength",
"non-ageing",
"surface-finish",
"surface-quality",
"enamelability",
"bc",
"bf",
"bt",
"bw/me",
"bl",
"m",
"chrom",
"phos",
"cbond",
"marvi",
"exptl",
"fe... | [
true,
true,
true,
false,
false,
true,
true,
true,
false,
true,
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,
true,
true,
true
] | 2,284 |
4,592 | 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
] | 2,285 |
3,978 | predictive_accuracy | accuracy_score | AP_Prostate_Kidney | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
1: [1 - 1007_s_at (numeric)],
2: [2 - 121_at (numeric)],
3: [3 - 1405_i_at (numeric)],
4: [4 - 1487_at (numeric)],
5: [5 - 1552256_a_at (numeric)],
6: [6 - 1552257_a_at (numeric)],
7: [7 - 1552274_at (numeric)],
8: [8 - 1552275_s_at (numeric)],
9: [9 - 1552302_at (numeric)],
10: [1... | {'MajorityClassSize': 260.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 69.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 329.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Prostate_Kidney | [
"1007_s_at",
"121_at",
"1405_i_at",
"1487_at",
"1552256_a_at",
"1552257_a_at",
"1552274_at",
"1552275_s_at",
"1552302_at",
"1552309_a_at",
"1552316_a_at",
"1552362_a_at",
"1552365_at",
"1552367_a_at",
"1552426_a_at",
"1552455_at",
"1552463_at",
"1552477_a_at",
"1552509_a_at",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,286 |
4,582 | 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
] | 2,287 |
3,974 | predictive_accuracy | accuracy_score | AP_Ovary_Lung | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
1: [1 - 1007_s_at (numeric)],
2: [2 - 121_at (numeric)],
3: [3 - 1405_i_at (numeric)],
4: [4 - 1552256_a_at (numeric)],
5: [5 - 1552257_a_at (numeric)],
6: [6 - 1552283_s_at (numeric)],
7: [7 - 1552348_at (numeric)],
8: [8 - 1552349_a_at (numeric)],
9: [9 - 1552365_at (numeric)],
1... | {'MajorityClassSize': 198.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 126.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10937.0,
'NumberOfInstances': 324.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10936.0,
'NumberOfSymbolicFeatures': ... | AP_Ovary_Lung | [
"1007_s_at",
"121_at",
"1405_i_at",
"1552256_a_at",
"1552257_a_at",
"1552283_s_at",
"1552348_at",
"1552349_a_at",
"1552365_at",
"1552368_at",
"1552426_a_at",
"1552477_a_at",
"1552566_at",
"1552594_at",
"1552610_a_at",
"1552619_a_at",
"1552621_at",
"1552622_s_at",
"1552626_a_at",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,288 |
4,589 | predictive_accuracy | accuracy_score | page-blocks | **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 - height (numeric)],
1: [1 - lenght (numeric)],
2: [2 - area (numeric)],
3: [3 - eccen (numeric)],
4: [4 - p_black (numeric)],
5: [5 - p_and (numeric)],
6: [6 - mean_tr (numeric)],
7: [7 - blackpix (numeric)],
8: [8 - blackand (numeric)],
9: [9 - wb_trans (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 4913.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 560.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 5473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,... | page-blocks | [
"height",
"lenght",
"area",
"eccen",
"p_black",
"p_and",
"mean_tr",
"blackpix",
"blackand",
"wb_trans"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,290 |
4,593 | predictive_accuracy | accuracy_score | grub-damage | **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 - year_zone (nominal)],
1: [1 - year (nominal)],
2: [2 - strip (numeric)],
3: [3 - pdk (numeric)],
4: [4 - damage_rankRJT (nominal)],
5: [5 - damage_rankALL (nominal)],
6: [6 - dry_or_irr (nominal)],
7: [7 - zone (nominal)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 106.0,
'MaxNominalAttDistinctValues': 21.0,
'MinorityClassSize': 49.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 155.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | grub-damage | [
"year_zone",
"year",
"strip",
"pdk",
"damage_rankRJT",
"damage_rankALL",
"dry_or_irr",
"zone"
] | [
true,
true,
false,
false,
true,
true,
true,
true
] | 2,291 |
4,605 | predictive_accuracy | accuracy_score | usp05 | **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 - ObjType (nominal)],
2: [2 - Effort (numeric)],
3: [3 - FunctPercent (nominal)],
4: [4 - IntComplx (nominal)],
5: [5 - DataFile (nominal)],
6: [6 - DataEn (nominal)],
7: [7 - DataOut (nominal)],
8: [8 - UFP (nominal)],
9: [9 - Lang (nominal)],
10: [10 - Tools (nominal)],
11: [1... | {'MajorityClassSize': 112.0,
'MaxNominalAttDistinctValues': 112.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 203.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 14.0,
... | usp05 | [
"ID",
"ObjType",
"Effort",
"FunctPercent",
"IntComplx",
"DataFile",
"DataEn",
"DataOut",
"UFP",
"Lang",
"Tools",
"ToolExpr",
"AppExpr",
"TeamSize",
"DBMS",
"Method"
] | [
false,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true
] | 2,292 |
4,606 | predictive_accuracy | accuracy_score | jEdit_4.2_4.3 | null | {0: [0 - WMC (numeric)],
1: [1 - DIT (numeric)],
2: [2 - NOC (numeric)],
3: [3 - CBO (numeric)],
4: [4 - RFC (numeric)],
5: [5 - LCOM (numeric)],
6: [6 - NPM (numeric)],
7: [7 - LOC (numeric)],
8: [8 - Bug-count (nominal)]} | {'MajorityClassSize': 204.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 165.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 369.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | jEdit_4.2_4.3 | [
"WMC",
"DIT",
"NOC",
"CBO",
"RFC",
"LCOM",
"NPM",
"LOC"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 2,293 |
4,617 | predictive_accuracy | accuracy_score | ar5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - total_loc (numeric)],
1: [1 - blank_loc (numeric)],
2: [2 - comment_loc (numeric)],
3: [3 - code_and_comment_loc (numeric)],
4: [4 - executable_loc (numeric)],
5: [5 - unique_operands (numeric)],
6: [6 - unique_operators (numeric)],
7: [7 - total_operands (numeric)],
8: [8 - total_operators (numeric)],... | {'MajorityClassSize': 28.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 8.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 36.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 29.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | ar5 | [
"total_loc",
"blank_loc",
"comment_loc",
"code_and_comment_loc",
"executable_loc",
"unique_operands",
"unique_operators",
"total_operands",
"total_operators",
"halstead_vocabulary",
"halstead_length",
"halstead_volume",
"halstead_level",
"halstead_difficulty",
"halstead_effort",
"halst... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,294 |
4,603 | predictive_accuracy | accuracy_score | kc1-top5 | **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 mode... | {0: [0 - PERCENT_PUB_DATA (numeric)],
1: [1 - ACCESS_TO_PUB_DATA (numeric)],
2: [2 - COUPLING_BETWEEN_OBJECTS (numeric)],
3: [3 - DEPTH (numeric)],
4: [4 - LACK_OF_COHESION_OF_METHODS (numeric)],
5: [5 - NUM_OF_CHILDREN (numeric)],
6: [6 - DEP_ON_CHILD (numeric)],
7: [7 - FAN_IN (numeric)],
8: [8 - RESPONSE_FOR... | {'MajorityClassSize': 137.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 8.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 95.0,
'NumberOfInstances': 145.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 94.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | kc1-top5 | [
"PERCENT_PUB_DATA",
"ACCESS_TO_PUB_DATA",
"COUPLING_BETWEEN_OBJECTS",
"DEPTH",
"LACK_OF_COHESION_OF_METHODS",
"NUM_OF_CHILDREN",
"DEP_ON_CHILD",
"FAN_IN",
"RESPONSE_FOR_CLASS",
"WEIGHTED_METHODS_PER_CLASS",
"minLOC_BLANK",
"minBRANCH_COUNT",
"minLOC_CODE_AND_COMMENT",
"minLOC_COMMENTS",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,295 |
4,611 | predictive_accuracy | accuracy_score | cm1_req | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - ACTION (numeric)],
1: [1 - CONDITIONAL (numeric)],
2: [2 - CONTINUANCE (numeric)],
3: [3 - IMPERATIVE (numeric)],
4: [4 - OPTION (numeric)],
5: [5 - RISK_LEVEL (nominal)],
6: [6 - SOURCE (numeric)],
7: [7 - WEAK_PHRASE (numeric)],
8: [8 - DEFECT (nominal)]} | {'MajorityClassSize': 69.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 89.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | cm1_req | [
"ACTION",
"CONDITIONAL",
"CONTINUANCE",
"IMPERATIVE",
"OPTION",
"RISK_LEVEL",
"SOURCE",
"WEAK_PHRASE"
] | [
false,
false,
false,
false,
false,
true,
false,
false
] | 2,296 |
4,615 | predictive_accuracy | accuracy_score | ar3 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - total_loc (numeric)],
1: [1 - blank_loc (numeric)],
2: [2 - comment_loc (numeric)],
3: [3 - code_and_comment_loc (numeric)],
4: [4 - executable_loc (numeric)],
5: [5 - unique_operands (numeric)],
6: [6 - unique_operators (numeric)],
7: [7 - total_operands (numeric)],
8: [8 - total_operators (numeric)],... | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 8.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 63.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 29.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | ar3 | [
"total_loc",
"blank_loc",
"comment_loc",
"code_and_comment_loc",
"executable_loc",
"unique_operands",
"unique_operators",
"total_operands",
"total_operators",
"halstead_vocabulary",
"halstead_length",
"halstead_volume",
"halstead_level",
"halstead_difficulty",
"halstead_effort",
"halst... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,297 |
4,610 | 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... | 2,298 |
4,568 | predictive_accuracy | accuracy_score | hypothyroid | **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 - on thyroxine (nominal)],
3: [3 - query on thyroxine (nominal)],
4: [4 - on antithyroid medication (nominal)],
5: [5 - sick (nominal)],
6: [6 - pregnant (nominal)],
7: [7 - thyroid surgery (nominal)],
8: [8 - I131 treatment (nominal)],
9: [9 - query hypot... | {'MajorityClassSize': 3481.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 291.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 3772.0,
'NumberOfInstancesWithMissingValues': 3772.0,
'NumberOfMissingValues': 6064.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures':... | hypothyroid | [
"age",
"sex",
"on thyroxine",
"query on thyroxine",
"on antithyroid medication",
"sick",
"pregnant",
"thyroid surgery",
"I131 treatment",
"query hypothyroid",
"query hyperthyroid",
"lithium",
"goitre",
"tumor",
"hypopituitary",
"psych",
"TSH measured",
"TSH",
"T3 measured",
"T3... | [
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
false,
true,
false,
true,
false,
true,
false,
true,
false,
true
] | 2,299 |
4,585 | predictive_accuracy | accuracy_score | arrhythmia | **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 - height (numeric)],
3: [3 - weight (numeric)],
4: [4 - QRSduration (numeric)],
5: [5 - PRinterval (numeric)],
6: [6 - Q-Tinterval (numeric)],
7: [7 - Tinterval (numeric)],
8: [8 - Pinterval (numeric)],
9: [9 - QRS (numeric)],
10: [10 - T (numeric)],
11:... | {'MajorityClassSize': 245.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 207.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 280.0,
'NumberOfInstances': 452.0,
'NumberOfInstancesWithMissingValues': 384.0,
'NumberOfMissingValues': 408.0,
'NumberOfNumericFeatures': 206.0,
'NumberOfSymbolicFeatures': ... | arrhythmia | [
"age",
"sex",
"height",
"weight",
"QRSduration",
"PRinterval",
"Q-Tinterval",
"Tinterval",
"Pinterval",
"QRS",
"T",
"P",
"QRST",
"J",
"heartrate",
"chDI_Qwave",
"chDI_Rwave",
"chDI_Swave",
"chDI_RPwave",
"chDI_SPwave",
"chDI_intrinsicReflecttions",
"chDI_RRwaveExists",
"c... | [
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
true,
tr... | 2,300 |
4,616 | predictive_accuracy | accuracy_score | ar4 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - total_loc (numeric)],
1: [1 - blank_loc (numeric)],
2: [2 - comment_loc (numeric)],
3: [3 - code_and_comment_loc (numeric)],
4: [4 - executable_loc (numeric)],
5: [5 - unique_operands (numeric)],
6: [6 - unique_operators (numeric)],
7: [7 - total_operands (numeric)],
8: [8 - total_operators (numeric)],... | {'MajorityClassSize': 87.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 107.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 29.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | ar4 | [
"total_loc",
"blank_loc",
"comment_loc",
"code_and_comment_loc",
"executable_loc",
"unique_operands",
"unique_operators",
"total_operands",
"total_operators",
"halstead_vocabulary",
"halstead_length",
"halstead_volume",
"halstead_level",
"halstead_difficulty",
"halstead_effort",
"halst... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,301 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.