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 |
|---|---|---|---|---|---|---|---|---|---|---|
4,302 | predictive_accuracy | accuracy_score | baskball | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - assists_per_minute (numeric)],
1: [1 - height (numeric)],
2: [2 - time_played (numeric)],
3: [3 - age (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 49.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 96.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | baskball | [
"assists_per_minute",
"height",
"time_played",
"age"
] | [
false,
false,
false,
false
] | 1,993 |
4,297 | predictive_accuracy | accuracy_score | fri_c2_100_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 40.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | fri_c2_100_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 1,994 |
4,218 | predictive_accuracy | accuracy_score | ipums_la_99-small | **Author**: IPUMS (ipums@hist.umn.edu)
**Donor**: Stephen Bay (sbay@ics.uci.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/IPUMS+Census+Database) - 1999
**Please cite**:
**IPUMS Database**
This data set contains unweighted PUMS census data from the Los Angeles and Long Beach areas for the y... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (nominal)],
6: [6 - rent (nominal)],
7: [7 - ftotinc (nominal)],
8: [8 - nfams (nominal)],
9: [9 - ncouples (nominal)],
10: [10 - nmothers (nominal)],
11: [11 - nfa... | {'MajorityClassSize': 5803.0,
'MaxNominalAttDistinctValues': 3890.0,
'MinorityClassSize': 197.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 8844.0,
'NumberOfInstancesWithMissingValues': 8844.0,
'NumberOfMissingValues': 51515.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatur... | ipums_la_99-small | [
"year",
"gq",
"gqtypeg",
"farm",
"ownershg",
"value",
"rent",
"ftotinc",
"nfams",
"ncouples",
"nmothers",
"nfathers",
"momloc",
"stepmom",
"momrule",
"poploc",
"steppop",
"poprule",
"sploc",
"sprule",
"famsize",
"nchild",
"nchlt5",
"famunit",
"eldch",
"yngch",
"ns... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 1,995 |
4,288 | predictive_accuracy | accuracy_score | rmftsa_ladata | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Total_Mortality (numeric)],
1: [1 - Respiratory_Mortality (numeric)],
2: [2 - Cardiovascular_Mortality (numeric)],
3: [3 - Temperature (numeric)],
4: [4 - Relative_Humidity (numeric)],
5: [5 - Carbon_Monoxide (numeric)],
6: [6 - Sulfur_Dioxideglm.LAshumway (numeric)],
7: [7 - Nitrogen_Dioxide (numeric)]... | {'MajorityClassSize': 286.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 222.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 508.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | rmftsa_ladata | [
"Total_Mortality",
"Respiratory_Mortality",
"Cardiovascular_Mortality",
"Temperature",
"Relative_Humidity",
"Carbon_Monoxide",
"Sulfur_Dioxideglm.LAshumway",
"Nitrogen_Dioxide",
"Hydrocarbons",
"Ozone"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,996 |
4,296 | predictive_accuracy | accuracy_score | bank8FM | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - a1cx (numeric)],
1: [1 - a1cy (numeric)],
2: [2 - b2x (numeric)],
3: [3 - b2y (numeric)],
4: [4 - a2pop (numeric)],
5: [5 - a3pop (numeric)],
6: [6 - temp (numeric)],
7: [7 - mxql (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 4885.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 3307.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
... | bank8FM | [
"a1cx",
"a1cy",
"b2x",
"b2y",
"a2pop",
"a3pop",
"temp",
"mxql"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,997 |
4,290 | predictive_accuracy | accuracy_score | veteran | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - treatment (nominal)],
1: [1 - celltype (nominal)],
2: [2 - status (nominal)],
3: [3 - karnofsky (numeric)],
4: [4 - months (numeric)],
5: [5 - age (numeric)],
6: [6 - therapy (nominal)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 94.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 43.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 137.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
'cos... | veteran | [
"treatment",
"celltype",
"status",
"karnofsky",
"months",
"age",
"therapy"
] | [
true,
true,
true,
false,
false,
false,
true
] | 1,998 |
4,303 | predictive_accuracy | accuracy_score | fri_c0_250_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 133.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 117.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_250_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,999 |
3,841 | predictive_accuracy | accuracy_score | mfeat-factors | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - att1 (numeric)],
1: [1 - att2 (numeric)],
2: [2 - att3 (numeric)],
3: [3 - att4 (numeric)],
4: [4 - att5 (numeric)],
5: [5 - att6 (numeric)],
6: [6 - att7 (numeric)],
7: [7 - att8 (numeric)],
8: [8 - att9 (numeric)],
9: [9 - att10 (numeric)],
10: [10 - att11 (numeric)],
11: [11 - att12 (numeric)],
... | {'MajorityClassSize': 1800.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 200.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 217.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 216.0,
'NumberOfSymbolicFeatures': 1.... | mfeat-factors | [
"att1",
"att2",
"att3",
"att4",
"att5",
"att6",
"att7",
"att8",
"att9",
"att10",
"att11",
"att12",
"att13",
"att14",
"att15",
"att16",
"att17",
"att18",
"att19",
"att20",
"att21",
"att22",
"att23",
"att24",
"att25",
"att26",
"att27",
"att28",
"att29",
"att30... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,001 |
4,291 | predictive_accuracy | accuracy_score | abalone | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Sex (nominal)],
1: [1 - Length (numeric)],
2: [2 - Diameter (numeric)],
3: [3 - Height (numeric)],
4: [4 - Whole weight (numeric)],
5: [5 - Shucked weight (numeric)],
6: [6 - Viscera weight (numeric)],
7: [7 - Shell weight (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 2096.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 2081.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 4177.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 2.0,
... | abalone | [
"Sex",
"Length",
"Diameter",
"Height",
"Whole weight",
"Shucked weight",
"Viscera weight",
"Shell weight"
] | [
true,
false,
false,
false,
false,
false,
false,
false
] | 2,002 |
4,309 | predictive_accuracy | accuracy_score | pharynx | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Inst (nominal)],
1: [1 - sex (nominal)],
2: [2 - Treatment (nominal)],
3: [3 - Grade (nominal)],
4: [4 - Age (numeric)],
5: [5 - Condition (nominal)],
6: [6 - Site (nominal)],
7: [7 - T (nominal)],
8: [8 - N (nominal)],
9: [9 - Entry (nominal)],
10: [10 - Status (nominal)],
11: [11 - binaryClass (no... | {'MajorityClassSize': 121.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 74.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 195.0,
'NumberOfInstancesWithMissingValues': 2.0,
'NumberOfMissingValues': 2.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 10.0,
'... | pharynx | [
"Inst",
"sex",
"Treatment",
"Grade",
"Age",
"Condition",
"Site",
"T",
"N",
"Status"
] | [
true,
true,
true,
true,
false,
true,
true,
true,
true,
true
] | 2,003 |
4,221 | predictive_accuracy | accuracy_score | ipums_la_98-small | **Author**: IPUMS (ipums@hist.umn.edu)
**Donor**: Stephen Bay (sbay@ics.uci.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/IPUMS+Census+Database) - 1999
**Please cite**:
**IPUMS Database**
This data set contains unweighted PUMS census data from the Los Angeles and Long Beach areas for the y... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (nominal)],
6: [6 - rent (nominal)],
7: [7 - ftotinc (nominal)],
8: [8 - nfams (nominal)],
9: [9 - ncouples (nominal)],
10: [10 - nmothers (nominal)],
11: [11 - nfa... | {'MajorityClassSize': 4802.0,
'MaxNominalAttDistinctValues': 3594.0,
'MinorityClassSize': 71.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 7485.0,
'NumberOfInstancesWithMissingValues': 7485.0,
'NumberOfMissingValues': 52048.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeature... | ipums_la_98-small | [
"year",
"gq",
"gqtypeg",
"farm",
"ownershg",
"value",
"rent",
"ftotinc",
"nfams",
"ncouples",
"nmothers",
"nfathers",
"momloc",
"stepmom",
"momrule",
"poploc",
"steppop",
"poprule",
"sploc",
"sprule",
"famsize",
"nchild",
"nchlt5",
"famunit",
"eldch",
"yngch",
"ns... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 2,004 |
4,286 | predictive_accuracy | accuracy_score | fri_c3_1000_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 557.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 443.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_1000_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,005 |
4,294 | predictive_accuracy | accuracy_score | fri_c4_1000_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 547.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 453.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_1000_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,006 |
4,310 | predictive_accuracy | accuracy_score | sleep | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - body_weight (numeric)],
1: [1 - brain_weight (numeric)],
2: [2 - max_life_span (numeric)],
3: [3 - gestation_time (numeric)],
4: [4 - predation_index (numeric)],
5: [5 - sleep_exposure_index (numeric)],
6: [6 - danger_index (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 33.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 62.0,
'NumberOfInstancesWithMissingValues': 7.0,
'NumberOfMissingValues': 8.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | sleep | [
"body_weight",
"brain_weight",
"max_life_span",
"gestation_time",
"predation_index",
"sleep_exposure_index",
"danger_index"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,007 |
4,314 | predictive_accuracy | accuracy_score | fri_c1_1000_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 543.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 457.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'... | fri_c1_1000_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,008 |
4,318 | predictive_accuracy | accuracy_score | servo | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - motor (nominal)],
1: [1 - screw (nominal)],
2: [2 - pgain (nominal)],
3: [3 - vgain (nominal)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 129.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 38.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 167.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 5.0,
'co... | servo | [
"motor",
"screw",
"pgain",
"vgain"
] | [
true,
true,
true,
true
] | 2,009 |
4,319 | predictive_accuracy | accuracy_score | analcatdata_wildcat | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Grievances (numeric)],
1: [1 - Rotate (nominal)],
2: [2 - Union (nominal)],
3: [3 - Workforce (numeric)],
4: [4 - Log_workforce (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 116.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 163.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | analcatdata_wildcat | [
"Grievances",
"Rotate",
"Union",
"Workforce",
"Log_workforce"
] | [
false,
true,
true,
false,
false
] | 2,010 |
4,193 | predictive_accuracy | accuracy_score | yeast_ml8 | **Author**:
**Source**: Unknown -
**Please cite**:
Yeast dataset
Past Usage:
André Elisseeff and Jason Weston.
A kernel method for multi-labelled classification.
In Thomas G. Dietterich, Susan Becker, and Zoubin Ghahramani, editors, Advances in Neural Information Processing Systems 14, 2002. | {0: [0 - attr1 (numeric)],
1: [1 - attr2 (numeric)],
2: [2 - attr3 (numeric)],
3: [3 - attr4 (numeric)],
4: [4 - attr5 (numeric)],
5: [5 - attr6 (numeric)],
6: [6 - attr7 (numeric)],
7: [7 - attr8 (numeric)],
8: [8 - attr9 (numeric)],
9: [9 - attr10 (numeric)],
10: [10 - attr11 (numeric)],
11: [11 - attr12 (... | {'MajorityClassSize': 2383.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 34.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 117.0,
'NumberOfInstances': 2417.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 103.0,
'NumberOfSymbolicFeatures': 14.... | yeast_ml8 | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6",
"attr7",
"attr8",
"attr9",
"attr10",
"attr11",
"attr12",
"attr13",
"attr14",
"attr15",
"attr16",
"attr17",
"attr18",
"attr19",
"attr20",
"attr21",
"attr22",
"attr23",
"attr24",
"attr25",
"attr26",
"attr27",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,011 |
4,311 | predictive_accuracy | accuracy_score | fri_c3_1000_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 560.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 440.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_1000_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,012 |
4,312 | predictive_accuracy | accuracy_score | rmftsa_sleepdata | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - heart_rate (numeric)],
1: [1 - sleep_state (nominal)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 515.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 509.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1024.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'... | rmftsa_sleepdata | [
"heart_rate",
"sleep_state"
] | [
false,
true
] | 2,013 |
4,321 | predictive_accuracy | accuracy_score | pm10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - pm10_concentration (numeric)],
1: [1 - cars_per_hour (numeric)],
2: [2 - temperature_at_2m (numeric)],
3: [3 - wind_speed (numeric)],
4: [4 - temperature_diff_2m_25m (numeric)],
5: [5 - wind_direction (numeric)],
6: [6 - hour_of_day (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 254.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 246.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | pm10 | [
"pm10_concentration",
"cars_per_hour",
"temperature_at_2m",
"wind_speed",
"temperature_diff_2m_25m",
"wind_direction",
"hour_of_day"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,014 |
4,315 | predictive_accuracy | accuracy_score | fri_c3_250_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 141.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 109.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c3_250_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,015 |
4,308 | predictive_accuracy | accuracy_score | space_ga | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - ln(VOTES/POP) (numeric)],
1: [1 - POP (numeric)],
2: [2 - EDUCATION (numeric)],
3: [3 - HOUSES (numeric)],
4: [4 - INCOME (numeric)],
5: [5 - XCOORD (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 1566.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 1541.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 3107.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | space_ga | [
"ln(VOTES/POP)",
"POP",
"EDUCATION",
"HOUSES",
"INCOME",
"XCOORD"
] | [
false,
false,
false,
false,
false,
false
] | 2,016 |
4,320 | predictive_accuracy | accuracy_score | fri_c3_500_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 263.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 237.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c3_500_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,017 |
4,316 | predictive_accuracy | accuracy_score | auto_price | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - symboling (nominal)],
1: [1 - normalized-losses (numeric)],
2: [2 - wheel-base (numeric)],
3: [3 - length (numeric)],
4: [4 - width (numeric)],
5: [5 - height (numeric)],
6: [6 - curb-weight (numeric)],
7: [7 - engine-size (numeric)],
8: [8 - bore (numeric)],
9: [9 - stroke (numeric)],
10: [10 - comp... | {'MajorityClassSize': 105.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 54.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 2.0,
'... | auto_price | [
"symboling",
"normalized-losses",
"wheel-base",
"length",
"width",
"height",
"curb-weight",
"engine-size",
"bore",
"stroke",
"compression-ratio",
"horsepower",
"peak-rpm",
"city-mpg",
"highway-mpg"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,018 |
4,325 | predictive_accuracy | accuracy_score | fri_c0_100_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 54.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 46.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | fri_c0_100_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,019 |
211,957 | predictive_accuracy | accuracy_score | slashdot | Multi-label dataset for text-classification. It consists of
article titles and partial blurbs. Blurbs can be assigned to several
categories (e.g. Science, News, Games) based on word predictors. | {0: [0 - Entertainment (nominal)],
1: [1 - Interviews (nominal)],
2: [2 - Main (nominal)],
3: [3 - Developers (nominal)],
4: [4 - Apache (nominal)],
5: [5 - News (nominal)],
6: [6 - Search (nominal)],
7: [7 - Mobile (nominal)],
8: [8 - Science (nominal)],
9: [9 - IT (nominal)],
10: [10 - BSD (nominal)],
11: ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 1101.0,
'NumberOfInstances': 3782.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1079.0,
'NumberOfSymbolicFeatures': 22.0,... | slashdot | [
"Entertainment",
"Interviews",
"Main",
"Developers",
"Apache",
"News",
"Search",
"Mobile",
"Science",
"IT",
"BSD",
"Idle",
"Games",
"YourRightsOnline",
"AskSlashdot",
"Apple",
"BookReviews",
"Hardware",
"Meta",
"Linux",
"Politics",
"Technology",
"X0",
"X000",
"X1",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false... | 2,020 |
4,317 | predictive_accuracy | accuracy_score | fri_c1_250_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 143.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 107.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_250_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,021 |
4,306 | predictive_accuracy | accuracy_score | cpu_small | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - runqsz (numeric)],
10: [10 - freemem (numeric)],
11: [11 - freeswa... | {'MajorityClassSize': 5715.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 2477.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 1.0... | cpu_small | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"runqsz",
"freemem",
"freeswap"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,022 |
4,222 | predictive_accuracy | accuracy_score | ipums_la_97-small | **Author**: IPUMS (ipums@hist.umn.edu)
**Donor**: Stephen Bay (sbay@ics.uci.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/IPUMS+Census+Database) - 1999
**Please cite**:
**IPUMS Database**
This data set contains unweighted PUMS census data from the Los Angeles and Long Beach areas for the y... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (nominal)],
6: [6 - rent (nominal)],
7: [7 - ftotinc (nominal)],
8: [8 - nfams (nominal)],
9: [9 - ncouples (nominal)],
10: [10 - nmothers (nominal)],
11: [11 - nfa... | {'MajorityClassSize': 1938.0,
'MaxNominalAttDistinctValues': 488.0,
'MinorityClassSize': 258.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 7019.0,
'NumberOfInstancesWithMissingValues': 7019.0,
'NumberOfMissingValues': 48089.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeature... | ipums_la_97-small | [
"year",
"gq",
"gqtypeg",
"farm",
"ownershg",
"value",
"rent",
"ftotinc",
"nfams",
"ncouples",
"nmothers",
"nfathers",
"momloc",
"stepmom",
"momrule",
"poploc",
"steppop",
"poprule",
"sploc",
"sprule",
"famsize",
"nchild",
"nchlt5",
"famunit",
"eldch",
"yngch",
"ns... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 2,023 |
4,326 | predictive_accuracy | accuracy_score | sleuth_ex1605 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - FMED (numeric)],
1: [1 - TMIQ (numeric)],
2: [2 - Age2IQ (numeric)],
3: [3 - Age4IQ (numeric)],
4: [4 - Age8IQ (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 31.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 31.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 62.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | sleuth_ex1605 | [
"FMED",
"TMIQ",
"Age2IQ",
"Age4IQ",
"Age8IQ"
] | [
false,
false,
false,
false,
false
] | 2,024 |
4,313 | predictive_accuracy | accuracy_score | fri_c4_500_100 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 283.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 217.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,... | fri_c4_500_100 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,025 |
4,322 | predictive_accuracy | accuracy_score | fri_c4_1000_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 560.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 440.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_1000_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,026 |
4,324 | predictive_accuracy | accuracy_score | wisconsin | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - lymph_node_status (numeric)],
1: [1 - radius_mean (numeric)],
2: [2 - radius_se (numeric)],
3: [3 - radius_worst (numeric)],
4: [4 - texture_mean (numeric)],
5: [5 - texture_se (numeric)],
6: [6 - texture_worst (numeric)],
7: [7 - perimeter_mean (numeric)],
8: [8 - perimeter_se (numeric)],
9: [9 - per... | {'MajorityClassSize': 104.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 90.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 194.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0,
'... | wisconsin | [
"lymph_node_status",
"radius_mean",
"radius_se",
"radius_worst",
"texture_mean",
"texture_se",
"texture_worst",
"perimeter_mean",
"perimeter_se",
"perimeter_worst",
"area_mean",
"area_se",
"area_worst",
"smoothness_mean",
"smoothness_se",
"smoothness_worst",
"compactness_mean",
"co... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,027 |
4,333 | predictive_accuracy | accuracy_score | fri_c2_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 45.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c2_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,029 |
4,331 | predictive_accuracy | accuracy_score | analcatdata_uktrainacc | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Year (numeric)],
1: [1 - Train_km (numeric)],
2: [2 - Pct_Mark_I (numeric)],
3: [3 - Accidents (numeric)],
4: [4 - SPAD_preventable (numeric)],
5: [5 - Other_preventable (numeric)],
6: [6 - Non_preventable (numeric)],
7: [7 - Year_grouped (numeric)],
8: [8 - Accidents_grouped (numeric)],
9: [9 - SPAD_... | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 4.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 31.0,
'NumberOfInstancesWithMissingValues': 25.0,
'NumberOfMissingValues': 150.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 1.0,
'... | analcatdata_uktrainacc | [
"Train_km",
"Pct_Mark_I",
"Accidents",
"SPAD_preventable",
"Other_preventable",
"Non_preventable",
"Year_grouped",
"Accidents_grouped",
"SPAD_grouped",
"Other_grouped",
"Non_grouped",
"Train_km_grouped",
"Fatalities",
"SPAD_fatalities",
"Other_fatalities"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,030 |
4,336 | predictive_accuracy | accuracy_score | analcatdata_apnea2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Automatic (nominal)],
1: [1 - Scorer_1 (nominal)],
2: [2 - Subject (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 411.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 64.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 475.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | analcatdata_apnea2 | [
"Automatic",
"Scorer_1",
"Subject"
] | [
true,
true,
false
] | 2,031 |
4,329 | predictive_accuracy | accuracy_score | analcatdata_election2000 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - County (nominal)],
1: [1 - Gore00 (numeric)],
2: [2 - Bush00 (numeric)],
3: [3 - Buchanan00 (numeric)],
4: [4 - Nader00 (numeric)],
5: [5 - Browne00 (numeric)],
6: [6 - Hagelin00 (numeric)],
7: [7 - Harris00 (numeric)],
8: [8 - McReynolds00 (numeric)],
9: [9 - Moorehead00 (numeric)],
10: [10 - Philli... | {'MajorityClassSize': 49.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 18.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 67.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | analcatdata_election2000 | [
"Gore00",
"Bush00",
"Buchanan00",
"Nader00",
"Browne00",
"Hagelin00",
"Harris00",
"McReynolds00",
"Moorehead00",
"Phillips00",
"Total00",
"Clinton96",
"Dole96",
"Perot96"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,032 |
4,327 | predictive_accuracy | accuracy_score | autoPrice | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - symboling (numeric)],
1: [1 - normalized-losses (numeric)],
2: [2 - wheel-base (numeric)],
3: [3 - length (numeric)],
4: [4 - width (numeric)],
5: [5 - height (numeric)],
6: [6 - curb-weight (numeric)],
7: [7 - engine-size (numeric)],
8: [8 - bore (numeric)],
9: [9 - stroke (numeric)],
10: [10 - comp... | {'MajorityClassSize': 105.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 54.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 1.0,
'... | autoPrice | [
"symboling",
"normalized-losses",
"wheel-base",
"length",
"width",
"height",
"curb-weight",
"engine-size",
"bore",
"stroke",
"compression-ratio",
"horsepower",
"peak-rpm",
"city-mpg",
"highway-mpg"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,033 |
4,335 | predictive_accuracy | accuracy_score | analcatdata_apnea3 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Automatic (nominal)],
1: [1 - Scorer_2 (nominal)],
2: [2 - Subject (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 395.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 55.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 450.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | analcatdata_apnea3 | [
"Automatic",
"Scorer_2",
"Subject"
] | [
true,
true,
false
] | 2,034 |
4,337 | predictive_accuracy | accuracy_score | fri_c1_500_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 262.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 238.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_500_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 2,035 |
4,334 | predictive_accuracy | accuracy_score | fri_c0_250_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 125.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 125.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_250_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,036 |
4,328 | predictive_accuracy | accuracy_score | meta | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - DS_Name (nominal)],
1: [1 - T (numeric)],
2: [2 - N (numeric)],
3: [3 - p (numeric)],
4: [4 - k (numeric)],
5: [5 - Bin (numeric)],
6: [6 - Cost (numeric)],
7: [7 - SDratio (numeric)],
8: [8 - correl (numeric)],
9: [9 - cancor1 (numeric)],
10: [10 - cancor2 (numeric)],
11: [11 - fract1 (numeric)],
... | {'MajorityClassSize': 474.0,
'MaxNominalAttDistinctValues': 24.0,
'MinorityClassSize': 54.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 528.0,
'NumberOfInstancesWithMissingValues': 264.0,
'NumberOfMissingValues': 504.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 3.... | meta | [
"DS_Name",
"T",
"N",
"p",
"k",
"Bin",
"Cost",
"SDratio",
"correl",
"cancor1",
"cancor2",
"fract1",
"fract2",
"skewness",
"kurtosis",
"Hc",
"Hx",
"MCx",
"EnAtr",
"NSRatio",
"Alg_Name"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 2,037 |
4,342 | predictive_accuracy | accuracy_score | analcatdata_michiganacc | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Time_index (numeric)],
1: [1 - Season (nominal)],
2: [2 - Month (nominal)],
3: [3 - Unemployment_rate (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 48.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 108.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | analcatdata_michiganacc | [
"Season",
"Month",
"Unemployment_rate"
] | [
true,
true,
false
] | 2,038 |
4,341 | predictive_accuracy | accuracy_score | strikes | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - country_code (numeric)],
1: [1 - year (numeric)],
2: [2 - strike_volume (numeric)],
3: [3 - unemployment (numeric)],
4: [4 - inflation (numeric)],
5: [5 - parliamentary_representation (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 315.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 310.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 625.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | strikes | [
"country_code",
"year",
"strike_volume",
"unemployment",
"inflation",
"parliamentary_representation"
] | [
false,
false,
false,
false,
false,
false
] | 2,039 |
4,339 | predictive_accuracy | accuracy_score | fri_c3_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 45.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c3_100_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,040 |
4,289 | predictive_accuracy | accuracy_score | fri_c4_1000_100 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 564.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 436.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | fri_c4_1000_100 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,041 |
4,343 | predictive_accuracy | accuracy_score | quake | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - focal_depth (numeric)],
1: [1 - latitude (numeric)],
2: [2 - longitude (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 1209.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 969.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 2178.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
... | quake | [
"focal_depth",
"latitude",
"longitude"
] | [
false,
false,
false
] | 2,042 |
4,340 | predictive_accuracy | accuracy_score | fri_c1_250_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 137.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 113.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_250_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,043 |
3,950 | predictive_accuracy | accuracy_score | musk | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Dataset from the MLRR repository: http://axon.cs.byu.edu:5000/
More infos: https://archive.ics.uci.edu/ml/datasets/Musk+(Version+2) | {0: [0 - ID (numeric)],
1: [1 - molecule_name (nominal)],
2: [2 - conformation_name (nominal)],
3: [3 - f1 (numeric)],
4: [4 - f2 (numeric)],
5: [5 - f3 (numeric)],
6: [6 - f4 (numeric)],
7: [7 - f5 (numeric)],
8: [8 - f6 (numeric)],
9: [9 - f7 (numeric)],
10: [10 - f8 (numeric)],
11: [11 - f9 (numeric)],
1... | {'MajorityClassSize': 5581.0,
'MaxNominalAttDistinctValues': 102.0,
'MinorityClassSize': 1017.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 168.0,
'NumberOfInstances': 6598.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 166.0,
'NumberOfSymbolicFeatures':... | musk | [
"molecule_name",
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"f7",
"f8",
"f9",
"f10",
"f11",
"f12",
"f13",
"f14",
"f15",
"f16",
"f17",
"f18",
"f19",
"f20",
"f21",
"f22",
"f23",
"f24",
"f25",
"f26",
"f27",
"f28",
"f29",
"f30",
"f31",
"f32",
"f33",
"f34",
... | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
fa... | 2,044 |
4,346 | predictive_accuracy | accuracy_score | fri_c2_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 57.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 43.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c2_100_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,045 |
4,351 | predictive_accuracy | accuracy_score | rabe_265 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - binaryClass (nominal)]} | {'MajorityClassSize': 30.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 21.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 51.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | rabe_265 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6"
] | [
false,
false,
false,
false,
false,
false
] | 2,046 |
4,350 | predictive_accuracy | accuracy_score | fri_c1_500_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 267.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 233.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_500_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,047 |
4,338 | predictive_accuracy | accuracy_score | analcatdata_apnea1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Scorer_1 (nominal)],
1: [1 - Scorer_2 (nominal)],
2: [2 - Subject (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 414.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 61.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 475.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | analcatdata_apnea1 | [
"Scorer_1",
"Scorer_2",
"Subject"
] | [
true,
true,
false
] | 2,048 |
4,345 | predictive_accuracy | accuracy_score | disclosure_x_bias | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Age (numeric)],
1: [1 - Civil (numeric)],
2: [2 - Can/US (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 345.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 317.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 662.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | disclosure_x_bias | [
"Age",
"Civil",
"Can/US"
] | [
false,
false,
false
] | 2,049 |
4,347 | predictive_accuracy | accuracy_score | fri_c0_250_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 125.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 125.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c0_250_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,050 |
4,348 | predictive_accuracy | accuracy_score | sleuth_ex1714 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - zip (numeric)],
1: [1 - fire (numeric)],
2: [2 - theft (numeric)],
3: [3 - age (numeric)],
4: [4 - income (numeric)],
5: [5 - race (numeric)],
6: [6 - vol (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 47.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | sleuth_ex1714 | [
"zip",
"fire",
"theft",
"age",
"income",
"race",
"vol"
] | [
false,
false,
false,
false,
false,
false,
false
] | 2,051 |
4,349 | predictive_accuracy | accuracy_score | bodyfat | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Density (numeric)],
1: [1 - Age (numeric)],
2: [2 - Weight (numeric)],
3: [3 - Height (numeric)],
4: [4 - Neck (numeric)],
5: [5 - Chest (numeric)],
6: [6 - Abdomen (numeric)],
7: [7 - Hip (numeric)],
8: [8 - Thigh (numeric)],
9: [9 - Knee (numeric)],
10: [10 - Ankle (numeric)],
11: [11 - Biceps (nu... | {'MajorityClassSize': 128.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 124.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 252.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 1.0,
... | bodyfat | [
"Density",
"Age",
"Weight",
"Height",
"Neck",
"Chest",
"Abdomen",
"Hip",
"Thigh",
"Knee",
"Ankle",
"Biceps",
"Forearm",
"Wrist"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,052 |
4,358 | predictive_accuracy | accuracy_score | triazines | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - p1_polar (numeric)],
1: [1 - p1_size (numeric)],
2: [2 - p1_flex (numeric)],
3: [3 - p1_h_doner (numeric)],
4: [4 - p1_h_acceptor (numeric)],
5: [5 - p1_pi_doner (numeric)],
6: [6 - p1_pi_acceptor (numeric)],
7: [7 - p1_polarisable (numeric)],
8: [8 - p1_sigma (numeric)],
9: [9 - p1_branch (numeric)],... | {'MajorityClassSize': 109.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 77.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 186.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 60.0,
'NumberOfSymbolicFeatures': 1.0,
'... | triazines | [
"p1_polar",
"p1_size",
"p1_flex",
"p1_h_doner",
"p1_h_acceptor",
"p1_pi_doner",
"p1_pi_acceptor",
"p1_polarisable",
"p1_sigma",
"p1_branch",
"p2_polar",
"p2_size",
"p2_flex",
"p2_h_doner",
"p2_h_acceptor",
"p2_pi_doner",
"p2_pi_acceptor",
"p2_polarisable",
"p2_sigma",
"p2_branc... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,053 |
4,356 | predictive_accuracy | accuracy_score | cleveland | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - cp (nominal)],
3: [3 - trestbps (numeric)],
4: [4 - chol (numeric)],
5: [5 - fbs (nominal)],
6: [6 - restecg (nominal)],
7: [7 - thalach (numeric)],
8: [8 - exang (nominal)],
9: [9 - oldpeak (numeric)],
10: [10 - slope (nominal)],
11: [11 - ca (numeric... | {'MajorityClassSize': 164.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 139.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 303.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 6.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 8.0,
'... | cleveland | [
"age",
"sex",
"cp",
"trestbps",
"chol",
"fbs",
"restecg",
"thalach",
"exang",
"oldpeak",
"slope",
"ca",
"thal"
] | [
false,
true,
true,
false,
false,
true,
true,
false,
true,
false,
true,
false,
true
] | 2,054 |
4,357 | predictive_accuracy | accuracy_score | witmer_census_1980 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - STATE (nominal)],
1: [1 - OVER65Perc (numeric)],
2: [2 - MEDAGE (numeric)],
3: [3 - PERCAP$ (numeric)],
4: [4 - COLLEGEPerc (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | witmer_census_1980 | [
"OVER65Perc",
"MEDAGE",
"PERCAP$",
"COLLEGEPerc"
] | [
false,
false,
false,
false
] | 2,055 |
4,352 | predictive_accuracy | accuracy_score | rabe_266 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 63.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 57.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 120.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | rabe_266 | [
"col_1",
"col_2"
] | [
false,
false
] | 2,056 |
4,353 | predictive_accuracy | accuracy_score | fri_c3_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 40.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c3_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,057 |
4,355 | predictive_accuracy | accuracy_score | wind_correlations | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - latitude (numeric)],
1: [1 - longitude (numeric)],
2: [2 - station_1 (numeric)],
3: [3 - station_2 (numeric)],
4: [4 - station_3 (numeric)],
5: [5 - station_4 (numeric)],
6: [6 - station_5 (numeric)],
7: [7 - station_6 (numeric)],
8: [8 - station_7 (numeric)],
9: [9 - station_8 (numeric)],
10: [10 - ... | {'MajorityClassSize': 23.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 22.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 47.0,
'NumberOfInstances': 45.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 46.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | wind_correlations | [
"latitude",
"longitude",
"station_1",
"station_2",
"station_3",
"station_4",
"station_5",
"station_6",
"station_7",
"station_8",
"station_9",
"station_10",
"station_11",
"station_12",
"station_13",
"station_14",
"station_15",
"station_16",
"station_17",
"station_18",
"station... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,058 |
4,354 | predictive_accuracy | accuracy_score | newton_hema | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - id (nominal)],
1: [1 - weeks (numeric)],
2: [2 - cells_percentage (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 70.0,
'MaxNominalAttDistinctValues': 11.0,
'MinorityClassSize': 70.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 140.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | newton_hema | [
"id",
"weeks",
"cells_percentage"
] | [
true,
false,
false
] | 2,059 |
4,362 | predictive_accuracy | accuracy_score | fri_c2_500_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 298.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 202.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c2_500_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,060 |
4,332 | predictive_accuracy | accuracy_score | cpu_act | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - pgout (numeric)],
10: [10 - ppgout (numeric)],
11: [11 - pgfree (n... | {'MajorityClassSize': 5715.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 2477.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0... | cpu_act | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"pgout",
"ppgout",
"pgfree",
"pgscan",
"atch",
"pgin",
"ppgin",
"pflt",
"vflt",
"runqsz",
"freemem",
"freeswap"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,062 |
212,085 | predictive_accuracy | accuracy_score | slashdot | Multi-label dataset for text-classification. It consists of
article titles and partial blurbs. Blurbs can be assigned to several
categories (e.g. Science, News, Games) based on word predictors. | {0: [0 - Entertainment (nominal)],
1: [1 - Interviews (nominal)],
2: [2 - Main (nominal)],
3: [3 - Developers (nominal)],
4: [4 - Apache (nominal)],
5: [5 - News (nominal)],
6: [6 - Search (nominal)],
7: [7 - Mobile (nominal)],
8: [8 - Science (nominal)],
9: [9 - IT (nominal)],
10: [10 - BSD (nominal)],
11: ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 1101.0,
'NumberOfInstances': 3782.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1079.0,
'NumberOfSymbolicFeatures': 22.0,... | slashdot | [
"Entertainment",
"Interviews",
"Main",
"Developers",
"Apache",
"News",
"Search",
"Mobile",
"Science",
"IT",
"BSD",
"Idle",
"Games",
"YourRightsOnline",
"AskSlashdot",
"Apple",
"BookReviews",
"Hardware",
"Meta",
"Linux",
"Politics",
"Technology",
"X0",
"X000",
"X1",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false... | 2,063 |
4,363 | predictive_accuracy | accuracy_score | fri_c3_250_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 135.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 115.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c3_250_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,064 |
4,344 | predictive_accuracy | accuracy_score | fri_c0_250_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 126.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 124.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c0_250_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,065 |
4,361 | predictive_accuracy | accuracy_score | diabetes_numeric | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - age (numeric)],
1: [1 - deficit (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 17.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 43.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | diabetes_numeric | [
"age",
"deficit"
] | [
false,
false
] | 2,066 |
4,360 | predictive_accuracy | accuracy_score | elusage | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - average_temperature (numeric)],
1: [1 - month (nominal)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 31.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 55.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | elusage | [
"average_temperature",
"month"
] | [
false,
true
] | 2,067 |
4,359 | predictive_accuracy | accuracy_score | fri_c1_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 53.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c1_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,068 |
4,366 | predictive_accuracy | accuracy_score | cpu | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - vendor (nominal)],
1: [1 - MYCT (numeric)],
2: [2 - MMIN (numeric)],
3: [3 - MMAX (numeric)],
4: [4 - CACH (numeric)],
5: [5 - CHMIN (numeric)],
6: [6 - CHMAX (numeric)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 156.0,
'MaxNominalAttDistinctValues': 30.0,
'MinorityClassSize': 53.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | cpu | [
"vendor",
"MYCT",
"MMIN",
"MMAX",
"CACH",
"CHMIN",
"CHMAX"
] | [
true,
false,
false,
false,
false,
false,
false
] | 2,069 |
4,373 | predictive_accuracy | accuracy_score | delta_ailerons | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - RollRate (numeric)],
1: [1 - PitchRate (numeric)],
2: [2 - currPitch (numeric)],
3: [3 - currRoll (numeric)],
4: [4 - diffRollRate (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 3783.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 3346.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 7129.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
... | delta_ailerons | [
"RollRate",
"PitchRate",
"currPitch",
"currRoll",
"diffRollRate"
] | [
false,
false,
false,
false,
false
] | 2,070 |
4,368 | predictive_accuracy | accuracy_score | cholesterol | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - cp (nominal)],
3: [3 - trestbps (numeric)],
4: [4 - fbs (nominal)],
5: [5 - restecg (nominal)],
6: [6 - thalach (numeric)],
7: [7 - exang (nominal)],
8: [8 - oldpeak (numeric)],
9: [9 - slope (nominal)],
10: [10 - ca (numeric)],
11: [11 - thal (nominal... | {'MajorityClassSize': 166.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 137.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 303.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 6.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 8.0,
'... | cholesterol | [
"age",
"sex",
"cp",
"trestbps",
"fbs",
"restecg",
"thalach",
"exang",
"oldpeak",
"slope",
"ca",
"thal",
"num"
] | [
false,
true,
true,
false,
true,
true,
false,
true,
false,
true,
false,
true,
false
] | 2,071 |
4,378 | predictive_accuracy | accuracy_score | fri_c0_100_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 45.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c0_100_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,072 |
4,370 | predictive_accuracy | accuracy_score | pyrim | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - p1_polar (numeric)],
1: [1 - p1_size (numeric)],
2: [2 - p1_flex (numeric)],
3: [3 - p1_h_doner (numeric)],
4: [4 - p1_h_acceptor (numeric)],
5: [5 - p1_pi_doner (numeric)],
6: [6 - p1_pi_acceptor (numeric)],
7: [7 - p1_polarisable (numeric)],
8: [8 - p1_sigma (numeric)],
9: [9 - p2_polar (numeric)],
... | {'MajorityClassSize': 43.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 31.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 28.0,
'NumberOfInstances': 74.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | pyrim | [
"p1_polar",
"p1_size",
"p1_flex",
"p1_h_doner",
"p1_h_acceptor",
"p1_pi_doner",
"p1_pi_acceptor",
"p1_polarisable",
"p1_sigma",
"p2_polar",
"p2_size",
"p2_flex",
"p2_h_doner",
"p2_h_acceptor",
"p2_pi_doner",
"p2_pi_acceptor",
"p2_polarisable",
"p2_sigma",
"p3_polar",
"p3_size",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,073 |
4,369 | predictive_accuracy | accuracy_score | fri_c0_1000_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 503.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 497.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'... | fri_c0_1000_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,074 |
4,374 | predictive_accuracy | accuracy_score | hutsof99_logis | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Age (nominal)],
1: [1 - Gender (nominal)],
2: [2 - Location (nominal)],
3: [3 - Coherence (numeric)],
4: [4 - Maturity (numeric)],
5: [5 - Delay (numeric)],
6: [6 - Prosecute (nominal)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 36.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 34.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 70.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
'cost... | hutsof99_logis | [
"Age",
"Gender",
"Location",
"Coherence",
"Maturity",
"Delay",
"Prosecute"
] | [
true,
true,
true,
false,
false,
false,
true
] | 2,075 |
4,323 | predictive_accuracy | accuracy_score | puma32H | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - theta1 (numeric)],
1: [1 - theta2 (numeric)],
2: [2 - theta3 (numeric)],
3: [3 - theta4 (numeric)],
4: [4 - theta5 (numeric)],
5: [5 - theta6 (numeric)],
6: [6 - thetad1 (numeric)],
7: [7 - thetad2 (numeric)],
8: [8 - thetad3 (numeric)],
9: [9 - thetad4 (numeric)],
10: [10 - thetad5 (numeric)],
11: ... | {'MajorityClassSize': 4128.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 4064.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0... | puma32H | [
"theta1",
"theta2",
"theta3",
"theta4",
"theta5",
"theta6",
"thetad1",
"thetad2",
"thetad3",
"thetad4",
"thetad5",
"thetad6",
"tau1",
"tau2",
"tau3",
"tau4",
"tau5",
"dm1",
"dm2",
"dm3",
"dm4",
"dm5",
"da1",
"da2",
"da3",
"da4",
"da5",
"db1",
"db2",
"db3",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,076 |
4,365 | predictive_accuracy | accuracy_score | disclosure_x_tampered | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - Age (numeric)],
1: [1 - Civil (numeric)],
2: [2 - Can/US (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 335.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 327.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 662.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | disclosure_x_tampered | [
"Age",
"Civil",
"Can/US"
] | [
false,
false,
false
] | 2,077 |
4,375 | predictive_accuracy | accuracy_score | fri_c4_500_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 264.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 236.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_500_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,078 |
4,371 | predictive_accuracy | accuracy_score | chscase_funds | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (nominal)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 98.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 87.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 185.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | chscase_funds | [
"col_2",
"col_3"
] | [
false,
false
] | 2,079 |
4,381 | predictive_accuracy | accuracy_score | rmftsa_ctoarrivals | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - year (numeric)],
1: [1 - month (nominal)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 163.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 101.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 264.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'... | rmftsa_ctoarrivals | [
"year",
"month"
] | [
false,
true
] | 2,080 |
4,380 | predictive_accuracy | accuracy_score | pbc | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - D (nominal)],
1: [1 - Z1 (nominal)],
2: [2 - Z2 (numeric)],
3: [3 - Z3 (nominal)],
4: [4 - Z4 (nominal)],
5: [5 - Z5 (nominal)],
6: [6 - Z6 (nominal)],
7: [7 - Z7 (nominal)],
8: [8 - Z8 (numeric)],
9: [9 - Z9 (numeric)],
10: [10 - Z10 (numeric)],
11: [11 - Z11 (numeric)],
12: [12 - Z12 (numeric)],
... | {'MajorityClassSize': 230.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 188.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 418.0,
'NumberOfInstancesWithMissingValues': 142.0,
'NumberOfMissingValues': 1239.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 9... | pbc | [
"D",
"Z1",
"Z2",
"Z3",
"Z4",
"Z5",
"Z6",
"Z7",
"Z8",
"Z9",
"Z10",
"Z11",
"Z12",
"Z13",
"Z14",
"Z15",
"Z16",
"Z17"
] | [
true,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 2,081 |
4,383 | predictive_accuracy | accuracy_score | fri_c3_1000_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 563.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 437.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'... | fri_c3_1000_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,082 |
4,385 | predictive_accuracy | accuracy_score | chscase_vine1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (numeric)],
8: [8 - col_9 (numeric)],
9: [9 - binaryClass (nominal)]} | {'MajorityClassSize': 28.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 52.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | chscase_vine1 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,083 |
4,372 | predictive_accuracy | accuracy_score | pbcseq | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - case_number (numeric)],
1: [1 - number_of_days (numeric)],
2: [2 - status (numeric)],
3: [3 - drug (nominal)],
4: [4 - age (numeric)],
5: [5 - sex (nominal)],
6: [6 - day (nominal)],
7: [7 - presence_of_asictes (nominal)],
8: [8 - presence_of_hepatomegaly (nominal)],
9: [9 - presence_of_spiders (nomin... | {'MajorityClassSize': 973.0,
'MaxNominalAttDistinctValues': 1024.0,
'MinorityClassSize': 972.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 1945.0,
'NumberOfInstancesWithMissingValues': 832.0,
'NumberOfMissingValues': 1133.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures... | pbcseq | [
"case_number",
"number_of_days",
"status",
"drug",
"age",
"sex",
"day",
"presence_of_asictes",
"presence_of_hepatomegaly",
"presence_of_spiders",
"presence_of_edema",
"serum_bilirubin",
"serum_cholesterol",
"albumin",
"alkaline_phosphatase",
"SGOT",
"platelets",
"prothrombin_time"
... | [
false,
false,
false,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,084 |
4,384 | predictive_accuracy | accuracy_score | chscase_vine2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - binaryClass (nominal)]} | {'MajorityClassSize': 256.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 212.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 468.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | chscase_vine2 | [
"col_1",
"col_2"
] | [
false,
false
] | 2,085 |
4,382 | predictive_accuracy | accuracy_score | fri_c1_100_25 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 53.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 47.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c1_100_25 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,086 |
4,387 | predictive_accuracy | accuracy_score | diggle_table_a1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 25.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 23.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 48.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | diggle_table_a1 | [
"col_1",
"col_2",
"col_3",
"col_4"
] | [
false,
false,
false,
false
] | 2,087 |
4,394 | predictive_accuracy | accuracy_score | fri_c1_500_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 274.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 226.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c1_500_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,088 |
4,388 | predictive_accuracy | accuracy_score | diggle_table_a2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (nominal)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 165.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 145.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 310.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | diggle_table_a2 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8"
] | [
true,
false,
false,
false,
false,
false,
false,
false
] | 2,089 |
4,400 | predictive_accuracy | accuracy_score | fri_c2_250_10 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 159.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 91.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'... | fri_c2_250_10 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,090 |
4,390 | predictive_accuracy | accuracy_score | chatfield_4 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)],
4: [4 - col_5 (numeric)],
5: [5 - col_6 (numeric)],
6: [6 - col_7 (numeric)],
7: [7 - col_8 (numeric)],
8: [8 - col_9 (numeric)],
9: [9 - col_10 (numeric)],
10: [10 - col_11 (numeric)],
11: [11 - col_12 (... | {'MajorityClassSize': 142.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 93.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 235.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 1.0,
'... | chatfield_4 | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9",
"col_10",
"col_11",
"col_12"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,091 |
4,367 | predictive_accuracy | accuracy_score | fri_c4_1000_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 560.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 440.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
... | fri_c4_1000_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,092 |
4,377 | predictive_accuracy | accuracy_score | kin8nm | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - theta1 (numeric)],
1: [1 - theta2 (numeric)],
2: [2 - theta3 (numeric)],
3: [3 - theta4 (numeric)],
4: [4 - theta5 (numeric)],
5: [5 - theta6 (numeric)],
6: [6 - theta7 (numeric)],
7: [7 - theta8 (numeric)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 4168.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 4024.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
... | kin8nm | [
"theta1",
"theta2",
"theta3",
"theta4",
"theta5",
"theta6",
"theta7",
"theta8"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 2,093 |
4,399 | predictive_accuracy | accuracy_score | fri_c1_100_5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - binaryClass (nominal)]} | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 45.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | fri_c1_100_5 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5"
] | [
false,
false,
false,
false,
false
] | 2,094 |
4,401 | predictive_accuracy | accuracy_score | autoMpg | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - cylinders (nominal)],
1: [1 - displacement (numeric)],
2: [2 - horsepower (numeric)],
3: [3 - weight (numeric)],
4: [4 - acceleration (numeric)],
5: [5 - model (nominal)],
6: [6 - origin (nominal)],
7: [7 - binaryClass (nominal)]} | {'MajorityClassSize': 209.0,
'MaxNominalAttDistinctValues': 13.0,
'MinorityClassSize': 189.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 398.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 6.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 4.0,
'... | autoMpg | [
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
"model",
"origin"
] | [
true,
false,
false,
false,
false,
true,
true
] | 2,095 |
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