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 |
|---|---|---|---|---|---|---|---|---|---|---|
4,398 | predictive_accuracy | accuracy_score | fri_c4_100_100 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 53.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_100_100 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
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false,
false,
false,
false,
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false,
false,
false,
f... | 2,096 |
4,397 | predictive_accuracy | accuracy_score | disclosure_x_noise | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Age (numeric)],
1: [1 - Civil (numeric)],
2: [2 - Can/US (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 333.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 329.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 662.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | disclosure_x_noise | [
"Age",
"Civil",
"Can/US"
] | [
false,
false,
false
] | 2,097 |
4,396 | predictive_accuracy | accuracy_score | sensory | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Occasion (nominal)],
1: [1 - Judges (nominal)],
2: [2 - Interval (nominal)],
3: [3 - Sittings (nominal)],
4: [4 - Position (nominal)],
5: [5 - Squares (nominal)],
6: [6 - Rows (nominal)],
7: [7 - Columns (nominal)],
8: [8 - Halfplot (nominal)],
9: [9 - Trellis (nominal)],
10: [10 - Method (nominal)],... | {'MajorityClassSize': 337.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 239.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 576.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | sensory | [
"Occasion",
"Judges",
"Interval",
"Sittings",
"Position",
"Squares",
"Rows",
"Columns",
"Halfplot",
"Trellis",
"Method"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,098 |
4,406 | predictive_accuracy | accuracy_score | sleuth_case1102 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - brain (numeric)],
1: [1 - liver (numeric)],
2: [2 - time (numeric)],
3: [3 - treat (nominal)],
4: [4 - days (nominal)],
5: [5 - sex (nominal)],
6: [6 - weight (numeric)],
7: [7 - loss (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 19.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 15.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 34.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 4.0,
'cost... | sleuth_case1102 | [
"brain",
"liver",
"time",
"treat",
"days",
"sex",
"weight",
"loss"
] | [
false,
false,
false,
true,
true,
true,
false,
false
] | 2,099 |
4,402 | predictive_accuracy | accuracy_score | fri_c3_250_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 139.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 111.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_250_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
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false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,100 |
4,386 | predictive_accuracy | accuracy_score | puma8NH | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - theta1 (numeric)],
1: [1 - theta2 (numeric)],
2: [2 - theta3 (numeric)],
3: [3 - thetad1 (numeric)],
4: [4 - thetad2 (numeric)],
5: [5 - thetad3 (numeric)],
6: [6 - tau1 (numeric)],
7: [7 - tau2 (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 4114.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 4078.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
... | puma8NH | [
"theta1",
"theta2",
"theta3",
"thetad1",
"thetad2",
"thetad3",
"tau1",
"tau2"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 2,101 |
4,405 | predictive_accuracy | accuracy_score | analcatdata_vehicle | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Alcohol-related (nominal)],
1: [1 - Gender (nominal)],
2: [2 - Type (nominal)],
3: [3 - Age (nominal)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 21.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 48.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 5.0,
'cost... | analcatdata_vehicle | [
"Alcohol-related",
"Gender",
"Type",
"Age"
] | [
true,
true,
true,
true
] | 2,102 |
4,389 | predictive_accuracy | accuracy_score | delta_elevators | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - climbRate (numeric)],
1: [1 - Altitude (numeric)],
2: [2 - RollRate (numeric)],
3: [3 - curRoll (numeric)],
4: [4 - diffClb (numeric)],
5: [5 - diffDiffClb (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 4785.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 4732.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 9517.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | delta_elevators | [
"climbRate",
"Altitude",
"RollRate",
"curRoll",
"diffClb",
"diffDiffClb"
] | [
false,
false,
false,
false,
false,
false
] | 2,103 |
4,395 | predictive_accuracy | accuracy_score | boston_corrected | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - OBS. (numeric)],
1: [1 - TOWN (nominal)],
2: [2 - TOWN_ID (numeric)],
3: [3 - TRACT (numeric)],
4: [4 - LON (numeric)],
5: [5 - LAT (numeric)],
6: [6 - MEDV (numeric)],
7: [7 - CMEDV (numeric)],
8: [8 - CRIM (numeric)],
9: [9 - ZN (numeric)],
10: [10 - INDUS (numeric)],
11: [11 - CHAS (nominal)],
1... | {'MajorityClassSize': 283.0,
'MaxNominalAttDistinctValues': 92.0,
'MinorityClassSize': 223.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 506.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 4.0,
... | boston_corrected | [
"OBS.",
"TOWN",
"TOWN_ID",
"TRACT",
"LON",
"LAT",
"MEDV",
"CMEDV",
"CRIM",
"ZN",
"INDUS",
"CHAS",
"NOX",
"RM",
"AGE",
"DIS",
"RAD",
"TAX",
"PTRATIO",
"B"
] | [
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
true,
false,
false,
false
] | 2,104 |
3,985 | predictive_accuracy | accuracy_score | AP_Endometrium_Omentum | **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 - 1552256_a_at (numeric)],
6: [6 - 1552289_a_at (numeric)],
7: [7 - 1552309_a_at (numeric)],
8: [8 - 1552347_at (numeric)],
9: [9 - 1552348_at (numeric)],
10: [1... | {'MajorityClassSize': 77.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 61.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 138.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1.... | AP_Endometrium_Omentum | [
"1007_s_at",
"121_at",
"1405_i_at",
"1438_at",
"1552256_a_at",
"1552289_a_at",
"1552309_a_at",
"1552347_at",
"1552348_at",
"1552349_a_at",
"1552368_at",
"1552426_a_at",
"1552456_a_at",
"1552594_at",
"1552610_a_at",
"1552619_a_at",
"1552621_at",
"1552622_s_at",
"1552628_a_at",
"... | [
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f... | 2,105 |
4,404 | predictive_accuracy | accuracy_score | fri_c4_250_100 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 140.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 110.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,... | fri_c4_250_100 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
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f... | 2,106 |
4,418 | predictive_accuracy | accuracy_score | schlvote | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - vote (nominal)],
1: [1 - tax_rate (numeric)],
2: [2 - budget (numeric)],
3: [3 - budget_change (numeric)],
4: [4 - tax_rate_change (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 28.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 10.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 38.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | schlvote | [
"vote",
"tax_rate",
"budget",
"budget_change",
"tax_rate_change"
] | [
true,
false,
false,
false,
false
] | 2,107 |
4,408 | predictive_accuracy | accuracy_score | fri_c4_500_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 284.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 216.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_500_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,108 |
4,415 | predictive_accuracy | accuracy_score | fri_c0_1000_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 509.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 491.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_1000_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,109 |
4,376 | predictive_accuracy | accuracy_score | fri_c3_1000_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 555.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 445.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_1000_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
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false,
false,
false,
false,
false,
false,
false,
f... | 2,110 |
4,410 | predictive_accuracy | accuracy_score | autoHorse | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - symboling (numeric)],
1: [1 - normalized-losses (numeric)],
2: [2 - make (nominal)],
3: [3 - fuel-type (nominal)],
4: [4 - aspiration (nominal)],
5: [5 - num-of-doors (numeric)],
6: [6 - body-style (nominal)],
7: [7 - drive-wheels (nominal)],
8: [8 - engine-location (nominal)],
9: [9 - wheel-base (num... | {'MajorityClassSize': 122.0,
'MaxNominalAttDistinctValues': 22.0,
'MinorityClassSize': 83.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 205.0,
'NumberOfInstancesWithMissingValues': 46.0,
'NumberOfMissingValues': 57.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 9.0,... | autoHorse | [
"symboling",
"normalized-losses",
"make",
"fuel-type",
"aspiration",
"num-of-doors",
"body-style",
"drive-wheels",
"engine-location",
"wheel-base",
"length",
"width",
"height",
"curb-weight",
"engine-type",
"num-of-cylinders",
"engine-size",
"fuel-system",
"bore",
"stroke",
"... | [
false,
false,
true,
true,
true,
false,
true,
true,
true,
false,
false,
false,
false,
false,
true,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false
] | 2,111 |
4,412 | predictive_accuracy | accuracy_score | analcatdata_runshoes | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Male (nominal)],
1: [1 - Married (nominal)],
2: [2 - Runs.per.week (numeric)],
3: [3 - Age (numeric)],
4: [4 - Income (numeric)],
5: [5 - College (nominal)],
6: [6 - Distance (nominal)],
7: [7 - Treadmill (nominal)],
8: [8 - Miles.per.week (numeric)],
9: [9 - Type.of.running (nominal)],
10: [10 - bin... | {'MajorityClassSize': 36.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 60.0,
'NumberOfInstancesWithMissingValues': 14.0,
'NumberOfMissingValues': 14.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | analcatdata_runshoes | [
"Male",
"Married",
"Runs.per.week",
"Age",
"Income",
"College",
"Distance",
"Treadmill",
"Miles.per.week",
"Type.of.running"
] | [
true,
true,
false,
false,
false,
true,
true,
true,
false,
true
] | 2,112 |
4,409 | predictive_accuracy | accuracy_score | kdd_el_nino-small | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - buoy (nominal)],
1: [1 - day (nominal)],
2: [2 - latitude (numeric)],
3: [3 - longitude (numeric)],
4: [4 - zon_winds (numeric)],
5: [5 - mer_winds (numeric)],
6: [6 - humidity (numeric)],
7: [7 - air_temp (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 508.0,
'MaxNominalAttDistinctValues': 59.0,
'MinorityClassSize': 274.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 782.0,
'NumberOfInstancesWithMissingValues': 214.0,
'NumberOfMissingValues': 466.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 3.0... | kdd_el_nino-small | [
"buoy",
"day",
"latitude",
"longitude",
"zon_winds",
"mer_winds",
"humidity",
"air_temp"
] | [
true,
true,
false,
false,
false,
false,
false,
false
] | 2,113 |
4,414 | predictive_accuracy | accuracy_score | breastTumor | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - age (numeric)],
1: [1 - menopause (nominal)],
2: [2 - inv-nodes (nominal)],
3: [3 - node-caps (nominal)],
4: [4 - deg-malig (nominal)],
5: [5 - breast (nominal)],
6: [6 - breast-quad (nominal)],
7: [7 - irradiation (nominal)],
8: [8 - recurrence (nominal)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 166.0,
'MaxNominalAttDistinctValues': 18.0,
'MinorityClassSize': 120.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 286.0,
'NumberOfInstancesWithMissingValues': 9.0,
'NumberOfMissingValues': 9.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 9.0,
... | breastTumor | [
"age",
"menopause",
"inv-nodes",
"node-caps",
"deg-malig",
"breast",
"breast-quad",
"irradiation",
"recurrence"
] | [
false,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,114 |
4,420 | predictive_accuracy | accuracy_score | fri_c0_100_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 51.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 49.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c0_100_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
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false,
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false,
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false,
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false,
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f... | 2,115 |
4,422 | predictive_accuracy | accuracy_score | analcatdata_gsssexsurvey | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Married (nominal)],
1: [1 - Age (numeric)],
2: [2 - Years_of_education (numeric)],
3: [3 - Male (nominal)],
4: [4 - Religious (nominal)],
5: [5 - Sex_partners (numeric)],
6: [6 - Income (numeric)],
7: [7 - Drug_use (nominal)],
8: [8 - Same_sex_relations (nominal)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 124.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 35.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 6.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 6.0,
'c... | analcatdata_gsssexsurvey | [
"Married",
"Age",
"Years_of_education",
"Male",
"Religious",
"Sex_partners",
"Income",
"Drug_use",
"Same_sex_relations"
] | [
true,
false,
false,
true,
true,
false,
false,
true,
true
] | 2,116 |
3,975 | predictive_accuracy | accuracy_score | AP_Endometrium_Prostate | **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 - 1552309_a_at (numeric)],
7: [7 - 1552348_at (numeric)],
8: [8 - 1552349_a_at (numeric)],
9: [9 - 1552365_at (numeric)],
10: [1... | {'MajorityClassSize': 69.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 61.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1.... | AP_Endometrium_Prostate | [
"1007_s_at",
"121_at",
"1405_i_at",
"1438_at",
"1552257_a_at",
"1552309_a_at",
"1552348_at",
"1552349_a_at",
"1552365_at",
"1552378_s_at",
"1552426_a_at",
"1552455_at",
"1552463_at",
"1552594_at",
"1552610_a_at",
"1552615_at",
"1552621_at",
"1552622_s_at",
"1552628_a_at",
"1552... | [
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false,
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false,
false,
false,
false,
false,
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false,
false,
false,
f... | 2,117 |
4,425 | predictive_accuracy | accuracy_score | fri_c4_500_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 276.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 224.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_500_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,118 |
4,421 | predictive_accuracy | accuracy_score | tecator | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - absorbance_1 (numeric)],
1: [1 - absorbance_2 (numeric)],
2: [2 - absorbance_3 (numeric)],
3: [3 - absorbance_4 (numeric)],
4: [4 - absorbance_5 (numeric)],
5: [5 - absorbance_6 (numeric)],
6: [6 - absorbance_7 (numeric)],
7: [7 - absorbance_8 (numeric)],
8: [8 - absorbance_9 (numeric)],
9: [9 - absor... | {'MajorityClassSize': 138.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 102.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 240.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 1.0,... | tecator | [
"absorbance_1",
"absorbance_2",
"absorbance_3",
"absorbance_4",
"absorbance_5",
"absorbance_6",
"absorbance_7",
"absorbance_8",
"absorbance_9",
"absorbance_10",
"absorbance_11",
"absorbance_12",
"absorbance_13",
"absorbance_14",
"absorbance_15",
"absorbance_16",
"absorbance_17",
"a... | [
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
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false,
false,
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f... | 2,119 |
4,429 | predictive_accuracy | accuracy_score | vinnie | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - year (numeric)],
1: [1 - field_goals (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 195.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 185.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 380.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | vinnie | [
"year",
"field_goals"
] | [
false,
false
] | 2,120 |
4,411 | predictive_accuracy | accuracy_score | stock | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - company1 (numeric)],
1: [1 - company2 (numeric)],
2: [2 - company3 (numeric)],
3: [3 - company4 (numeric)],
4: [4 - company5 (numeric)],
5: [5 - company6 (numeric)],
6: [6 - company7 (numeric)],
7: [7 - company8 (numeric)],
8: [8 - company9 (numeric)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 488.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 462.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 950.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'... | stock | [
"company1",
"company2",
"company3",
"company4",
"company5",
"company6",
"company7",
"company8",
"company9"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,121 |
4,428 | predictive_accuracy | accuracy_score | analcatdata_gviolence | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Title (nominal)],
1: [1 - Year (numeric)],
2: [2 - Length (numeric)],
3: [3 - Violence_time (numeric)],
4: [4 - Injuries (numeric)],
5: [5 - Fatal_injuries (numeric)],
6: [6 - Good/neutral_injuries (numeric)],
7: [7 - Good/neutral_fatalities (numeric)],
8: [8 - Bad_injuries (numeric)],
9: [9 - binaryC... | {'MajorityClassSize': 43.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 31.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 74.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | analcatdata_gviolence | [
"Year",
"Length",
"Violence_time",
"Injuries",
"Fatal_injuries",
"Good/neutral_injuries",
"Good/neutral_fatalities",
"Bad_injuries"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 2,122 |
4,430 | predictive_accuracy | accuracy_score | auto93 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Manufacturer (nominal)],
1: [1 - Type (nominal)],
2: [2 - City_MPG (numeric)],
3: [3 - Highway_MPG (numeric)],
4: [4 - Air_Bags_standard (nominal)],
5: [5 - Drive_train_type (nominal)],
6: [6 - Number_of_cylinders (numeric)],
7: [7 - Engine_size (numeric)],
8: [8 - Horsepower (numeric)],
9: [9 - RPM (... | {'MajorityClassSize': 58.0,
'MaxNominalAttDistinctValues': 31.0,
'MinorityClassSize': 35.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 93.0,
'NumberOfInstancesWithMissingValues': 11.0,
'NumberOfMissingValues': 14.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 7.0,
... | auto93 | [
"Manufacturer",
"Type",
"City_MPG",
"Highway_MPG",
"Air_Bags_standard",
"Drive_train_type",
"Number_of_cylinders",
"Engine_size",
"Horsepower",
"RPM",
"Engine_revolutions_per_mile",
"Manual_transmission_available",
"Fuel_tank_capacity",
"Passenger_capacity",
"Length",
"Wheelbase",
"W... | [
true,
true,
false,
false,
true,
true,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 2,123 |
4,424 | predictive_accuracy | accuracy_score | fishcatch | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Species (nominal)],
1: [1 - Length1 (numeric)],
2: [2 - Length2 (numeric)],
3: [3 - Length3 (numeric)],
4: [4 - Height (numeric)],
5: [5 - Width (numeric)],
6: [6 - Sex (nominal)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 95.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 63.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 158.0,
'NumberOfInstancesWithMissingValues': 87.0,
'NumberOfMissingValues': 87.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 3.0,
'c... | fishcatch | [
"Species",
"Length1",
"Length2",
"Length3",
"Height",
"Width",
"Sex"
] | [
true,
false,
false,
false,
false,
false,
true
] | 2,124 |
3,988 | predictive_accuracy | accuracy_score | AP_Endometrium_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 - 1438_at (numeric)],
6: [6 - 1552257_a_at (numeric)],
7: [7 - 1552283_s_at (numeric)],
8: [8 - 1552309_a_at (numeric)],
9: [9 - 1552348_at (numeric)],
10: [10 - ... | {'MajorityClassSize': 126.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 61.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 187.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Endometrium_Lung | [
"1007_s_at",
"117_at",
"121_at",
"1405_i_at",
"1438_at",
"1552257_a_at",
"1552283_s_at",
"1552309_a_at",
"1552348_at",
"1552349_a_at",
"1552365_at",
"1552368_at",
"1552426_a_at",
"1552477_a_at",
"1552594_at",
"1552610_a_at",
"1552615_at",
"1552619_a_at",
"1552621_at",
"1552622_... | [
false,
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false,
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f... | 2,125 |
4,431 | predictive_accuracy | accuracy_score | sleuth_ex2016 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - sv (nominal)],
1: [1 - ag (nominal)],
2: [2 - tl (numeric)],
3: [3 - ae (numeric)],
4: [4 - wt (numeric)],
5: [5 - bh (numeric)],
6: [6 - hl (numeric)],
7: [7 - fl (numeric)],
8: [8 - tt (numeric)],
9: [9 - sk (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 45.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 42.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 87.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 3.0,
'cos... | sleuth_ex2016 | [
"sv",
"ag",
"tl",
"ae",
"wt",
"bh",
"hl",
"fl",
"tt",
"sk"
] | [
true,
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,126 |
4,419 | predictive_accuracy | accuracy_score | fri_c0_1000_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 503.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 497.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_1000_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,127 |
4,423 | predictive_accuracy | accuracy_score | boston | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - CRIM (numeric)],
1: [1 - ZN (numeric)],
2: [2 - INDUS (numeric)],
3: [3 - CHAS (nominal)],
4: [4 - NOX (numeric)],
5: [5 - RM (numeric)],
6: [6 - AGE (numeric)],
7: [7 - DIS (numeric)],
8: [8 - RAD (numeric)],
9: [9 - TAX (numeric)],
10: [10 - PTRATIO (numeric)],
11: [11 - B (numeric)],
12: [12 - L... | {'MajorityClassSize': 297.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 209.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 506.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 2.0,
... | boston | [
"CRIM",
"ZN",
"INDUS",
"CHAS",
"NOX",
"RM",
"AGE",
"DIS",
"RAD",
"TAX",
"PTRATIO",
"B",
"LSTAT"
] | [
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,128 |
4,426 | predictive_accuracy | accuracy_score | bolts | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
SUMMARY:
Data from an experiment on the affects of machine adjustments on
the time to count bolts. Data appear as the STATS (Issue 10) Challenge.
DATA:
Submitted by W. Robert Stephenson, Iowa State University
email: wrstephe@iastate.edu
A man... | {0: [0 - RUN (numeric)],
1: [1 - SPEED1 (numeric)],
2: [2 - TOTAL (numeric)],
3: [3 - SPEED2 (numeric)],
4: [4 - NUMBER2 (numeric)],
5: [5 - SENS (numeric)],
6: [6 - TIME (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 14.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 40.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | bolts | [
"RUN",
"SPEED1",
"TOTAL",
"SPEED2",
"NUMBER2",
"SENS",
"TIME"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,129 |
4,417 | predictive_accuracy | accuracy_score | wind | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - year (numeric)],
1: [1 - month (numeric)],
2: [2 - day (numeric)],
3: [3 - RPT (numeric)],
4: [4 - VAL (numeric)],
5: [5 - ROS (numeric)],
6: [6 - KIL (numeric)],
7: [7 - SHA (numeric)],
8: [8 - BIR (numeric)],
9: [9 - DUB (numeric)],
10: [10 - CLA (numeric)],
11: [11 - MUL (numeric)],
12: [12 - CL... | {'MajorityClassSize': 3501.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 3073.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 6574.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 1.0... | wind | [
"year",
"month",
"day",
"RPT",
"VAL",
"ROS",
"KIL",
"SHA",
"BIR",
"DUB",
"CLA",
"MUL",
"CLO",
"BEL"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,130 |
4,433 | predictive_accuracy | accuracy_score | sleuth_ex2015 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - owl (nominal)],
1: [1 - pctring1 (numeric)],
2: [2 - pctring2 (numeric)],
3: [3 - pctring3 (numeric)],
4: [4 - pctring4 (numeric)],
5: [5 - pctring5 (numeric)],
6: [6 - pctring6 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 33.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 27.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 60.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | sleuth_ex2015 | [
"owl",
"pctring1",
"pctring2",
"pctring3",
"pctring4",
"pctring5",
"pctring6"
] | [
true,
false,
false,
false,
false,
false,
false
] | 2,131 |
4,427 | predictive_accuracy | accuracy_score | hungarian | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - cp (nominal)],
3: [3 - trestbps (numeric)],
4: [4 - chol (numeric)],
5: [5 - fbs (nominal)],
6: [6 - restecg (nominal)],
7: [7 - thalach (numeric)],
8: [8 - exang (nominal)],
9: [9 - oldpeak (numeric)],
10: [10 - slope (nominal)],
11: [11 - ca (numeric... | {'MajorityClassSize': 188.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 106.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 294.0,
'NumberOfInstancesWithMissingValues': 293.0,
'NumberOfMissingValues': 782.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 8.0... | hungarian | [
"age",
"sex",
"cp",
"trestbps",
"chol",
"fbs",
"restecg",
"thalach",
"exang",
"oldpeak",
"slope",
"ca",
"thal"
] | [
false,
true,
true,
false,
false,
true,
true,
false,
true,
false,
true,
false,
true
] | 2,132 |
4,432 | predictive_accuracy | accuracy_score | fri_c4_250_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 133.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 117.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_250_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,133 |
4,442 | predictive_accuracy | accuracy_score | fri_c3_250_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 142.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 108.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_250_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
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false,
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false,
false,
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false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,134 |
4,437 | predictive_accuracy | accuracy_score | fri_c4_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 54.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 46.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c4_100_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,136 |
3,959 | predictive_accuracy | accuracy_score | AP_Omentum_Prostate | **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 - 1552289_a_at (numeric)],
7: [7 - 1552309_a_at (numeric)],
8: [8 - 1552348_at (numeric)],
9: [9 - 1552365_at (numeric)],
1... | {'MajorityClassSize': 77.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 69.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 146.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1.... | AP_Omentum_Prostate | [
"1007_s_at",
"121_at",
"1405_i_at",
"1552256_a_at",
"1552257_a_at",
"1552289_a_at",
"1552309_a_at",
"1552348_at",
"1552365_at",
"1552368_at",
"1552426_a_at",
"1552455_at",
"1552456_a_at",
"1552463_at",
"1552477_a_at",
"1552610_a_at",
"1552615_at",
"1552619_a_at",
"1552621_at",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
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false,
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false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,137 |
4,439 | predictive_accuracy | accuracy_score | fri_c1_500_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 267.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 233.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c1_500_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,138 |
4,445 | predictive_accuracy | accuracy_score | fri_c1_100_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 56.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 44.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c1_100_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
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false,
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false,
false,
false,
false,
false,
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false,
false,
false,
false,
f... | 2,139 |
4,436 | predictive_accuracy | accuracy_score | visualizing_livestock | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - livestocktype (nominal)],
1: [1 - country (nominal)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 105.0,
'MaxNominalAttDistinctValues': 26.0,
'MinorityClassSize': 25.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 3.0,
'c... | visualizing_livestock | [
"livestocktype",
"country"
] | [
true,
true
] | 2,140 |
4,434 | predictive_accuracy | accuracy_score | analcatdata_neavote | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Name (nominal)],
1: [1 - Party (nominal)],
2: [2 - Favorable (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 93.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 7.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | analcatdata_neavote | [
"Party",
"Favorable"
] | [
true,
false
] | 2,142 |
4,449 | predictive_accuracy | accuracy_score | mu284 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - LABEL (numeric)],
1: [1 - P85 (numeric)],
2: [2 - P75 (numeric)],
3: [3 - RMT85 (numeric)],
4: [4 - CS82 (numeric)],
5: [5 - SS82 (numeric)],
6: [6 - S82 (numeric)],
7: [7 - ME84 (numeric)],
8: [8 - REV84 (numeric)],
9: [9 - REG (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 142.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 142.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 284.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | mu284 | [
"LABEL",
"P85",
"P75",
"RMT85",
"CS82",
"SS82",
"S82",
"ME84",
"REV84",
"REG"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,143 |
4,438 | predictive_accuracy | accuracy_score | fri_c2_500_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 286.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 214.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_500_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,144 |
4,444 | predictive_accuracy | accuracy_score | analcatdata_chlamydia | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Age (nominal)],
1: [1 - Gender (nominal)],
2: [2 - Race (nominal)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 81.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 19.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 4.0,
'co... | analcatdata_chlamydia | [
"Age",
"Gender",
"Race"
] | [
true,
true,
true
] | 2,145 |
4,447 | predictive_accuracy | accuracy_score | fri_c4_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 53.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c4_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,146 |
4,304 | predictive_accuracy | accuracy_score | machine_cpu | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - MYCT (numeric)],
1: [1 - MMIN (numeric)],
2: [2 - MMAX (numeric)],
3: [3 - CACH (numeric)],
4: [4 - CHMIN (numeric)],
5: [5 - CHMAX (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 153.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 56.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | machine_cpu | [
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
false,
false,
false,
false,
false,
false
] | 2,147 |
4,446 | predictive_accuracy | accuracy_score | fri_c2_250_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 137.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 113.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_250_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,148 |
4,301 | predictive_accuracy | accuracy_score | fri_c1_250_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 131.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 119.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c1_250_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,149 |
4,448 | predictive_accuracy | accuracy_score | fri_c2_500_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 304.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 196.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_500_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,150 |
4,407 | predictive_accuracy | accuracy_score | fri_c1_1000_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 547.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 453.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_1000_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,151 |
4,456 | predictive_accuracy | accuracy_score | fri_c0_500_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 256.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 244.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_500_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,152 |
4,451 | predictive_accuracy | accuracy_score | pollution | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - PREC (numeric)],
1: [1 - JANT (numeric)],
2: [2 - JULT (numeric)],
3: [3 - OVR65 (numeric)],
4: [4 - POPN (numeric)],
5: [5 - EDUC (numeric)],
6: [6 - HOUS (numeric)],
7: [7 - DENS (numeric)],
8: [8 - NONW (numeric)],
9: [9 - WWDRK (numeric)],
10: [10 - POOR (numeric)],
11: [11 - HC (numeric)],
12:... | {'MajorityClassSize': 31.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 60.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | pollution | [
"PREC",
"JANT",
"JULT",
"OVR65",
"POPN",
"EDUC",
"HOUS",
"DENS",
"NONW",
"WWDRK",
"POOR",
"HC",
"NOX",
"SO@",
"HUMID"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,153 |
4,440 | predictive_accuracy | accuracy_score | pollen | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - RIDGE (numeric)],
1: [1 - NUB (numeric)],
2: [2 - CRACK (numeric)],
3: [3 - WEIGHT (numeric)],
4: [4 - DENSITY (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 1924.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 1924.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 3848.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
... | pollen | [
"RIDGE",
"NUB",
"CRACK",
"WEIGHT",
"DENSITY"
] | [
false,
false,
false,
false,
false
] | 2,154 |
4,455 | predictive_accuracy | accuracy_score | mbagrade | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - sex (nominal)],
1: [1 - GMAT (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 32.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 61.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | mbagrade | [
"sex",
"GMAT"
] | [
true,
false
] | 2,155 |
3,998 | predictive_accuracy | accuracy_score | AP_Endometrium_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 - 1552261_at (numeric)],
7: [7 - 1552309_a_at (numeric)],
8: [8 - 1552348_at (numeric)],
9: [9 - 1552349_a_at (numeric)],
10: [1... | {'MajorityClassSize': 124.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 61.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 185.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Endometrium_Uterus | [
"1007_s_at",
"121_at",
"1405_i_at",
"1438_at",
"1552257_a_at",
"1552261_at",
"1552309_a_at",
"1552348_at",
"1552349_a_at",
"1552368_at",
"1552378_s_at",
"1552426_a_at",
"1552455_at",
"1552477_a_at",
"1552594_at",
"1552610_a_at",
"1552615_at",
"1552619_a_at",
"1552621_at",
"1552... | [
false,
false,
false,
false,
false,
false,
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false,
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false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,156 |
4,300 | predictive_accuracy | accuracy_score | visualizing_slope | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - error (numeric)],
1: [1 - percent (numeric)],
2: [2 - distance (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 17.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 44.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | visualizing_slope | [
"error",
"percent",
"distance"
] | [
false,
false,
false
] | 2,157 |
4,454 | predictive_accuracy | accuracy_score | no2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - no2_concentration (numeric)],
1: [1 - cars_per_hour (numeric)],
2: [2 - temperature_at_2m (numeric)],
3: [3 - wind_speed (numeric)],
4: [4 - temperature_diff_2m_25m (numeric)],
5: [5 - wind_direction (numeric)],
6: [6 - hour_of_day (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 251.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 249.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | no2 | [
"no2_concentration",
"cars_per_hour",
"temperature_at_2m",
"wind_speed",
"temperature_diff_2m_25m",
"wind_direction",
"hour_of_day"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,158 |
4,452 | predictive_accuracy | accuracy_score | fri_c0_500_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 251.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 249.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c0_500_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,159 |
4,299 | predictive_accuracy | accuracy_score | analcatdata_supreme | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Actions_taken (numeric)],
1: [1 - Liberal (numeric)],
2: [2 - Unconstitutional (numeric)],
3: [3 - Precedent_alteration (numeric)],
4: [4 - Unanimous (numeric)],
5: [5 - Year_of_decision (numeric)],
6: [6 - Lower_court_disagreement (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 3081.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 971.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 4052.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
... | analcatdata_supreme | [
"Actions_taken",
"Liberal",
"Unconstitutional",
"Precedent_alteration",
"Unanimous",
"Year_of_decision",
"Lower_court_disagreement"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,160 |
4,458 | predictive_accuracy | accuracy_score | cloud | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - PERIOD (numeric)],
1: [1 - SEEDED (nominal)],
2: [2 - TE (numeric)],
3: [3 - TW (numeric)],
4: [4 - NC (numeric)],
5: [5 - SC (numeric)],
6: [6 - NWC (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 76.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 32.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 108.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | cloud | [
"PERIOD",
"SEEDED",
"TE",
"TW",
"NC",
"SC",
"NWC"
] | [
false,
true,
false,
false,
false,
false,
false
] | 2,161 |
4,460 | predictive_accuracy | accuracy_score | sleuth_case1201 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - state (nominal)],
1: [1 - sat (numeric)],
2: [2 - takers (numeric)],
3: [3 - income (numeric)],
4: [4 - years (numeric)],
5: [5 - public (numeric)],
6: [6 - expend (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | sleuth_case1201 | [
"sat",
"takers",
"income",
"years",
"public",
"expend"
] | [
false,
false,
false,
false,
false,
false
] | 2,162 |
4,435 | predictive_accuracy | accuracy_score | fri_c2_1000_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 582.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 418.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_1000_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,163 |
4,459 | predictive_accuracy | accuracy_score | sleuth_case1202 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - bsal (numeric)],
1: [1 - sal77 (numeric)],
2: [2 - fsex (nominal)],
3: [3 - senior (numeric)],
4: [4 - age (numeric)],
5: [5 - educ (nominal)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 57.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 93.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 3.0,
'cost... | sleuth_case1202 | [
"bsal",
"sal77",
"fsex",
"senior",
"age",
"educ"
] | [
false,
false,
true,
false,
false,
true
] | 2,164 |
4,466 | predictive_accuracy | accuracy_score | hip | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - control_1 (numeric)],
1: [1 - control_2 (numeric)],
2: [2 - control_3 (numeric)],
3: [3 - control_4 (numeric)],
4: [4 - treatment_1 (numeric)],
5: [5 - treatment_2 (numeric)],
6: [6 - treatment_3 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 28.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 26.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 54.0,
'NumberOfInstancesWithMissingValues': 30.0,
'NumberOfMissingValues': 120.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | hip | [
"control_1",
"control_2",
"control_3",
"control_4",
"treatment_1",
"treatment_2",
"treatment_3"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,165 |
3,965 | predictive_accuracy | accuracy_score | AP_Prostate_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 - 1552261_at (numeric)],
7: [7 - 1552309_a_at (numeric)],
8: [8 - 1552348_at (numeric)],
9: [9 - 1552365_at (numeric)],
10: [10 ... | {'MajorityClassSize': 124.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 69.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 193.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Prostate_Uterus | [
"1007_s_at",
"121_at",
"1405_i_at",
"1438_at",
"1552257_a_at",
"1552261_at",
"1552309_a_at",
"1552348_at",
"1552365_at",
"1552378_s_at",
"1552426_a_at",
"1552455_at",
"1552463_at",
"1552477_a_at",
"1552610_a_at",
"1552615_at",
"1552619_a_at",
"1552621_at",
"1552622_s_at",
"1552... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,166 |
4,457 | predictive_accuracy | accuracy_score | fri_c0_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 50.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 50.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c0_100_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,167 |
4,467 | predictive_accuracy | accuracy_score | analcatdata_negotiation | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Role (nominal)],
1: [1 - Status (numeric)],
2: [2 - Trust (numeric)],
3: [3 - Outcome_favorability (numeric)],
4: [4 - Procedural_fairness (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 66.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 26.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 92.0,
'NumberOfInstancesWithMissingValues': 17.0,
'NumberOfMissingValues': 26.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | analcatdata_negotiation | [
"Role",
"Status",
"Trust",
"Outcome_favorability",
"Procedural_fairness"
] | [
true,
false,
false,
false,
false
] | 2,168 |
4,461 | predictive_accuracy | accuracy_score | visualizing_hamster | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - lung (numeric)],
1: [1 - heart (numeric)],
2: [2 - liver (numeric)],
3: [3 - spleen (numeric)],
4: [4 - kidney (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 33.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 73.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | visualizing_hamster | [
"lung",
"heart",
"liver",
"spleen",
"kidney"
] | [
false,
false,
false,
false,
false
] | 2,169 |
4,462 | predictive_accuracy | accuracy_score | rabe_148 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 33.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 33.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 66.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | rabe_148 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5"
] | [
false,
false,
false,
false,
false
] | 2,170 |
4,464 | predictive_accuracy | accuracy_score | fri_c3_500_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 280.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 220.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_500_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,171 |
4,468 | predictive_accuracy | accuracy_score | chscase_census6 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 235.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 165.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_census6 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6"
] | [
false,
false,
false,
false,
false,
false
] | 2,172 |
3,958 | predictive_accuracy | accuracy_score | AP_Omentum_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 - 1552256_a_at (numeric)],
6: [6 - 1552257_a_at (numeric)],
7: [7 - 1552309_a_at (numeric)],
8: [8 - 1552348_at (numeric)],
9: [9 - 1552368_at (numeric)],
10: [1... | {'MajorityClassSize': 124.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 77.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 201.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Omentum_Uterus | [
"1007_s_at",
"121_at",
"1405_i_at",
"1438_at",
"1552256_a_at",
"1552257_a_at",
"1552309_a_at",
"1552348_at",
"1552368_at",
"1552378_s_at",
"1552426_a_at",
"1552455_at",
"1552456_a_at",
"1552477_a_at",
"1552610_a_at",
"1552619_a_at",
"1552621_at",
"1552622_s_at",
"1552628_a_at",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
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false,
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false,
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f... | 2,173 |
4,463 | predictive_accuracy | accuracy_score | chscase_geyser1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 134.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 88.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 222.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | chscase_geyser1 | [
"col_1",
"col_2"
] | [
false,
false
] | 2,174 |
4,476 | predictive_accuracy | accuracy_score | chscase_census3 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 208.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 192.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_census3 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,175 |
4,465 | predictive_accuracy | accuracy_score | colleges_aaup | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - FICE (numeric)],
1: [1 - College_name (nominal)],
2: [2 - State (nominal)],
3: [3 - Type (nominal)],
4: [4 - Average_salary-full_professors (numeric)],
5: [5 - Average_salary-associate_professors (numeric)],
6: [6 - Average_salary-assistant_professors (numeric)],
7: [7 - Average_salary-all_ranks (numeri... | {'MajorityClassSize': 813.0,
'MaxNominalAttDistinctValues': 52.0,
'MinorityClassSize': 348.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 1161.0,
'NumberOfInstancesWithMissingValues': 87.0,
'NumberOfMissingValues': 256.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 3... | colleges_aaup | [
"FICE",
"State",
"Type",
"Average_salary-full_professors",
"Average_salary-associate_professors",
"Average_salary-assistant_professors",
"Average_salary-all_ranks",
"Average_compensation-full_professors",
"Average_compensation-associate_professors",
"Average_compensation-assistant_professors",
"... | [
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,176 |
4,470 | predictive_accuracy | accuracy_score | sleuth_case2002 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - FM (nominal)],
1: [1 - LC (nominal)],
2: [2 - BK (nominal)],
3: [3 - SS (nominal)],
4: [4 - AG (numeric)],
5: [5 - YR (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 78.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 69.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 147.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 5.0,
'cos... | sleuth_case2002 | [
"FM",
"LC",
"BK",
"SS",
"AG",
"YR"
] | [
true,
true,
true,
true,
false,
false
] | 2,177 |
4,473 | predictive_accuracy | accuracy_score | chscase_adopt | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (nominal)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 12.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 39.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | chscase_adopt | [
"col_2",
"col_3"
] | [
false,
false
] | 2,178 |
4,474 | predictive_accuracy | accuracy_score | chscase_census5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 207.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 193.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_census5 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,179 |
3,966 | predictive_accuracy | accuracy_score | AP_Omentum_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 - 1552365_at (numeric)],
9: [9 - 1552368_at (numeric)],
10:... | {'MajorityClassSize': 126.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 77.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 203.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': 1... | AP_Omentum_Lung | [
"1007_s_at",
"121_at",
"1405_i_at",
"1552256_a_at",
"1552257_a_at",
"1552283_s_at",
"1552348_at",
"1552365_at",
"1552368_at",
"1552426_a_at",
"1552456_a_at",
"1552477_a_at",
"1552610_a_at",
"1552615_at",
"1552619_a_at",
"1552621_at",
"1552622_s_at",
"1552626_a_at",
"1552628_a_at"... | [
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false,
false,
false,
false,
false,
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false,
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false,
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false,
false,
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false,
false,
f... | 2,180 |
4,403 | predictive_accuracy | accuracy_score | bank32nh | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - a1cx (numeric)],
1: [1 - a1cy (numeric)],
2: [2 - a1sx (numeric)],
3: [3 - a1sy (numeric)],
4: [4 - a1rho (numeric)],
5: [5 - a1pop (numeric)],
6: [6 - a2cx (numeric)],
7: [7 - a2cy (numeric)],
8: [8 - a2sx (numeric)],
9: [9 - a2sy (numeric)],
10: [10 - a2rho (numeric)],
11: [11 - a2pop (numeric)],
... | {'MajorityClassSize': 5649.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 2543.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0... | bank32nh | [
"a1cx",
"a1cy",
"a1sx",
"a1sy",
"a1rho",
"a1pop",
"a2cx",
"a2cy",
"a2sx",
"a2sy",
"a2rho",
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"a3rho",
"a3pop",
"temp",
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"b1y",
"b1call",
"b1eff",
"b2x",
"b2y",
"b2call",
"b2eff",
"b3x",
"b3y",
"b3call",
"b3eff",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,181 |
4,481 | predictive_accuracy | accuracy_score | fri_c2_1000_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 580.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 420.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_1000_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,182 |
4,475 | predictive_accuracy | accuracy_score | chscase_census4 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 206.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 194.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_census4 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,183 |
4,482 | predictive_accuracy | accuracy_score | balloon | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - id (numeric)], 1: [1 - raw (numeric)], 2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 1519.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 482.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 2001.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 1.0,
... | balloon | [
"raw"
] | [
false
] | 2,184 |
4,477 | predictive_accuracy | accuracy_score | chscase_census2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 203.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 197.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_census2 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,185 |
4,479 | predictive_accuracy | accuracy_score | fri_c2_250_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 140.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 110.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c2_250_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,186 |
4,471 | predictive_accuracy | accuracy_score | fri_c2_1000_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 563.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 437.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c2_1000_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,187 |
4,484 | predictive_accuracy | accuracy_score | fri_c3_100_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 56.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 44.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | fri_c3_100_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,188 |
4,487 | predictive_accuracy | accuracy_score | rabe_166 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 21.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 19.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 40.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | rabe_166 | [
"col_2"
] | [
false
] | 2,189 |
4,480 | predictive_accuracy | accuracy_score | fri_c2_1000_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 584.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 416.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'... | fri_c2_1000_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,190 |
4,478 | predictive_accuracy | accuracy_score | fri_c1_1000_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 564.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 436.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_1000_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,191 |
4,483 | predictive_accuracy | accuracy_score | plasma_retinol | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - AGE (numeric)],
1: [1 - SEX (nominal)],
2: [2 - SMOKSTAT (nominal)],
3: [3 - QUETELET (numeric)],
4: [4 - VITUSE (nominal)],
5: [5 - CALORIES (numeric)],
6: [6 - FAT (numeric)],
7: [7 - FIBER (numeric)],
8: [8 - ALCOHOL (numeric)],
9: [9 - CHOLESTEROL (numeric)],
10: [10 - BETADIET (numeric)],
11: [... | {'MajorityClassSize': 182.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 133.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 315.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 4.0,
... | plasma_retinol | [
"AGE",
"SEX",
"SMOKSTAT",
"QUETELET",
"VITUSE",
"CALORIES",
"FAT",
"FIBER",
"ALCOHOL",
"CHOLESTEROL",
"BETADIET",
"RETDIET",
"BETAPLASMA"
] | [
false,
true,
true,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,192 |
4,486 | predictive_accuracy | accuracy_score | fri_c4_250_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 135.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 115.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_250_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,193 |
3,977 | predictive_accuracy | accuracy_score | AP_Colon_Omentum | **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)],
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4,488 | predictive_accuracy | accuracy_score | fri_c2_500_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
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4,493 | predictive_accuracy | accuracy_score | visualizing_galaxy | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - eastwest (numeric)],
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4,495 | predictive_accuracy | accuracy_score | hutsof99_child_witness | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - subj (numeric)],
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