uid int64 2 364k | orig_metric stringclasses 30
values | sklearn_metric stringclasses 9
values | dataset_name stringlengths 2 124 | dataset_description stringlengths 3 13k ⌀ | dataset_features stringlengths 41 3.57M | task_description stringlengths 627 762 | task_name stringlengths 2 124 | attribute_names listlengths 0 100k | categorical_indicator listlengths 0 100k | __index_level_0__ int64 0 3.8k |
|---|---|---|---|---|---|---|---|---|---|---|
3,901 | predictive_accuracy | accuracy_score | jEdit_4.2_4.3 | null | {0: [0 - WMC (numeric)],
1: [1 - DIT (numeric)],
2: [2 - NOC (numeric)],
3: [3 - CBO (numeric)],
4: [4 - RFC (numeric)],
5: [5 - LCOM (numeric)],
6: [6 - NPM (numeric)],
7: [7 - LOC (numeric)],
8: [8 - Bug-count (nominal)]} | {'MajorityClassSize': 204.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 165.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 369.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | jEdit_4.2_4.3 | [
"WMC",
"DIT",
"NOC",
"CBO",
"RFC",
"LCOM",
"NPM",
"LOC"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,882 |
3,900 | predictive_accuracy | accuracy_score | usp05 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This is a PROMISE Software Engineering Repository data set made publicly
available in order to encourage repeatable, verifiable, refutable, and/or
improvable predictive mod... | {0: [0 - ID (numeric)],
1: [1 - ObjType (nominal)],
2: [2 - Effort (numeric)],
3: [3 - FunctPercent (nominal)],
4: [4 - IntComplx (nominal)],
5: [5 - DataFile (nominal)],
6: [6 - DataEn (nominal)],
7: [7 - DataOut (nominal)],
8: [8 - UFP (nominal)],
9: [9 - Lang (nominal)],
10: [10 - Tools (nominal)],
11: [1... | {'MajorityClassSize': 112.0,
'MaxNominalAttDistinctValues': 112.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 203.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 14.0,
... | usp05 | [
"ID",
"ObjType",
"Effort",
"FunctPercent",
"IntComplx",
"DataFile",
"DataEn",
"DataOut",
"UFP",
"Lang",
"Tools",
"ToolExpr",
"AppExpr",
"TeamSize",
"DBMS",
"Method"
] | [
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false,
true,
true,
true,
true,
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] | 1,883 |
3,910 | predictive_accuracy | accuracy_score | ar3 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - total_loc (numeric)],
1: [1 - blank_loc (numeric)],
2: [2 - comment_loc (numeric)],
3: [3 - code_and_comment_loc (numeric)],
4: [4 - executable_loc (numeric)],
5: [5 - unique_operands (numeric)],
6: [6 - unique_operators (numeric)],
7: [7 - total_operands (numeric)],
8: [8 - total_operators (numeric)],... | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 8.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 63.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 29.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | ar3 | [
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] | 1,884 |
3,888 | predictive_accuracy | accuracy_score | grub-damage | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - year_zone (nominal)],
1: [1 - year (nominal)],
2: [2 - strip (numeric)],
3: [3 - pdk (numeric)],
4: [4 - damage_rankRJT (nominal)],
5: [5 - damage_rankALL (nominal)],
6: [6 - dry_or_irr (nominal)],
7: [7 - zone (nominal)],
8: [8 - binaryClass (nominal)]} | {'MajorityClassSize': 106.0,
'MaxNominalAttDistinctValues': 21.0,
'MinorityClassSize': 49.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 155.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | grub-damage | [
"year_zone",
"year",
"strip",
"pdk",
"damage_rankRJT",
"damage_rankALL",
"dry_or_irr",
"zone"
] | [
true,
true,
false,
false,
true,
true,
true,
true
] | 1,885 |
3,863 | predictive_accuracy | accuracy_score | hypothyroid | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - on thyroxine (nominal)],
3: [3 - query on thyroxine (nominal)],
4: [4 - on antithyroid medication (nominal)],
5: [5 - sick (nominal)],
6: [6 - pregnant (nominal)],
7: [7 - thyroid surgery (nominal)],
8: [8 - I131 treatment (nominal)],
9: [9 - query hypot... | {'MajorityClassSize': 3481.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 291.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 3772.0,
'NumberOfInstancesWithMissingValues': 3772.0,
'NumberOfMissingValues': 6064.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures':... | hypothyroid | [
"age",
"sex",
"on thyroxine",
"query on thyroxine",
"on antithyroid medication",
"sick",
"pregnant",
"thyroid surgery",
"I131 treatment",
"query hypothyroid",
"query hyperthyroid",
"lithium",
"goitre",
"tumor",
"hypopituitary",
"psych",
"TSH measured",
"TSH",
"T3 measured",
"T3... | [
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] | 1,886 |
3,909 | predictive_accuracy | accuracy_score | ar1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
This is a PROMISE Software Engineering Repository data set made publicly
available in order to encourage repeatable, refutable, verifiable, and/or
improvable predictive m... | {0: [0 - total_loc (numeric)],
1: [1 - blank_loc (numeric)],
2: [2 - comment_loc (numeric)],
3: [3 - code_and_comment_loc (numeric)],
4: [4 - executable_loc (numeric)],
5: [5 - unique_operands (numeric)],
6: [6 - unique_operators (numeric)],
7: [7 - total_operands (numeric)],
8: [8 - total_operators (numeric)],... | {'MajorityClassSize': 112.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 9.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 121.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 29.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | ar1 | [
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] | 1,887 |
3,898 | predictive_accuracy | accuracy_score | kc1-top5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This is a PROMISE Software Engineering Repository data set made publicly
available in order to encourage repeatable, verifiable, refutable, and/or
improvable predictive mode... | {0: [0 - PERCENT_PUB_DATA (numeric)],
1: [1 - ACCESS_TO_PUB_DATA (numeric)],
2: [2 - COUPLING_BETWEEN_OBJECTS (numeric)],
3: [3 - DEPTH (numeric)],
4: [4 - LACK_OF_COHESION_OF_METHODS (numeric)],
5: [5 - NUM_OF_CHILDREN (numeric)],
6: [6 - DEP_ON_CHILD (numeric)],
7: [7 - FAN_IN (numeric)],
8: [8 - RESPONSE_FOR... | {'MajorityClassSize': 137.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 8.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 95.0,
'NumberOfInstances': 145.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 94.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | kc1-top5 | [
"PERCENT_PUB_DATA",
"ACCESS_TO_PUB_DATA",
"COUPLING_BETWEEN_OBJECTS",
"DEPTH",
"LACK_OF_COHESION_OF_METHODS",
"NUM_OF_CHILDREN",
"DEP_ON_CHILD",
"FAN_IN",
"RESPONSE_FOR_CLASS",
"WEIGHTED_METHODS_PER_CLASS",
"minLOC_BLANK",
"minBRANCH_COUNT",
"minLOC_CODE_AND_COMMENT",
"minLOC_COMMENTS",
... | [
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f... | 1,888 |
3,884 | predictive_accuracy | accuracy_score | page-blocks | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - height (numeric)],
1: [1 - lenght (numeric)],
2: [2 - area (numeric)],
3: [3 - eccen (numeric)],
4: [4 - p_black (numeric)],
5: [5 - p_and (numeric)],
6: [6 - mean_tr (numeric)],
7: [7 - blackpix (numeric)],
8: [8 - blackand (numeric)],
9: [9 - wb_trans (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 4913.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 560.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 5473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,... | page-blocks | [
"height",
"lenght",
"area",
"eccen",
"p_black",
"p_and",
"mean_tr",
"blackpix",
"blackand",
"wb_trans"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,889 |
3,911 | predictive_accuracy | accuracy_score | ar4 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - total_loc (numeric)],
1: [1 - blank_loc (numeric)],
2: [2 - comment_loc (numeric)],
3: [3 - code_and_comment_loc (numeric)],
4: [4 - executable_loc (numeric)],
5: [5 - unique_operands (numeric)],
6: [6 - unique_operators (numeric)],
7: [7 - total_operands (numeric)],
8: [8 - total_operators (numeric)],... | {'MajorityClassSize': 87.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 107.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 29.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | ar4 | [
"total_loc",
"blank_loc",
"comment_loc",
"code_and_comment_loc",
"executable_loc",
"unique_operands",
"unique_operators",
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] | 1,890 |
3,914 | predictive_accuracy | accuracy_score | ar6 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
This is a PROMISE Software Engineering Repository data set made publicly
available in order to encourage repeatable, refutable, verifiable, and/or
improvable predictive m... | {0: [0 - total_loc (numeric)],
1: [1 - blank_loc (numeric)],
2: [2 - comment_loc (numeric)],
3: [3 - code_and_comment_loc (numeric)],
4: [4 - executable_loc (numeric)],
5: [5 - unique_operands (numeric)],
6: [6 - unique_operators (numeric)],
7: [7 - total_operands (numeric)],
8: [8 - total_operators (numeric)],... | {'MajorityClassSize': 86.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 15.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 101.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 29.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | ar6 | [
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"blank_loc",
"comment_loc",
"code_and_comment_loc",
"executable_loc",
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] | 1,892 |
3,833 | predictive_accuracy | accuracy_score | analcatdata_authorship | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - a (numeric)],
1: [1 - all (numeric)],
2: [2 - also (numeric)],
3: [3 - an (numeric)],
4: [4 - and (numeric)],
5: [5 - any (numeric)],
6: [6 - are (numeric)],
7: [7 - as (numeric)],
8: [8 - at (numeric)],
9: [9 - be (numeric)],
10: [10 - been (numeric)],
11: [11 - but (numeric)],
12: [12 - by (numer... | {'MajorityClassSize': 524.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 317.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 71.0,
'NumberOfInstances': 841.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 70.0,
'NumberOfSymbolicFeatures': 1.0,
... | analcatdata_authorship | [
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f... | 1,893 |
3,852 | predictive_accuracy | accuracy_score | anneal | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - family (nominal)],
1: [1 - product-type (nominal)],
2: [2 - steel (nominal)],
3: [3 - carbon (numeric)],
4: [4 - hardness (numeric)],
5: [5 - temper_rolling (nominal)],
6: [6 - condition (nominal)],
7: [7 - formability (nominal)],
8: [8 - strength (numeric)],
9: [9 - non-ageing (nominal)],
10: [10 - ... | {'MajorityClassSize': 684.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 214.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 898.0,
'NumberOfMissingValues': 22175.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 3... | anneal | [
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"product-type",
"steel",
"carbon",
"hardness",
"temper_rolling",
"condition",
"formability",
"strength",
"non-ageing",
"surface-finish",
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"bf",
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"phos",
"cbond",
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"fe... | [
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] | 1,894 |
3,906 | predictive_accuracy | accuracy_score | cm1_req | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - ACTION (numeric)],
1: [1 - CONDITIONAL (numeric)],
2: [2 - CONTINUANCE (numeric)],
3: [3 - IMPERATIVE (numeric)],
4: [4 - OPTION (numeric)],
5: [5 - RISK_LEVEL (nominal)],
6: [6 - SOURCE (numeric)],
7: [7 - WEAK_PHRASE (numeric)],
8: [8 - DEFECT (nominal)]} | {'MajorityClassSize': 69.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 20.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 89.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | cm1_req | [
"ACTION",
"CONDITIONAL",
"CONTINUANCE",
"IMPERATIVE",
"OPTION",
"RISK_LEVEL",
"SOURCE",
"WEAK_PHRASE"
] | [
false,
false,
false,
false,
false,
true,
false,
false
] | 1,895 |
3,912 | predictive_accuracy | accuracy_score | ar5 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - total_loc (numeric)],
1: [1 - blank_loc (numeric)],
2: [2 - comment_loc (numeric)],
3: [3 - code_and_comment_loc (numeric)],
4: [4 - executable_loc (numeric)],
5: [5 - unique_operands (numeric)],
6: [6 - unique_operators (numeric)],
7: [7 - total_operands (numeric)],
8: [8 - total_operators (numeric)],... | {'MajorityClassSize': 28.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 8.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 30.0,
'NumberOfInstances': 36.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 29.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | ar5 | [
"total_loc",
"blank_loc",
"comment_loc",
"code_and_comment_loc",
"executable_loc",
"unique_operands",
"unique_operators",
"total_operands",
"total_operators",
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"halstead_length",
"halstead_volume",
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] | 1,896 |
3,923 | predictive_accuracy | accuracy_score | datatrieve | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This is a PROMISE Software Engineering Repository data set made publicly
available in order to encourage repeatable, verifiable, refutable, and/or
improvable predictive mode... | {0: [0 - LOC6_0 (numeric)],
1: [1 - LOC6_1 (numeric)],
2: [2 - Added_LoC (numeric)],
3: [3 - Del_LoC (numeric)],
4: [4 - Diff_Block (numeric)],
5: [5 - Mod_Rate (numeric)],
6: [6 - Mod_Know (numeric)],
7: [7 - ReusedLoC (numeric)],
8: [8 - Faulty6_1 (nominal)]} | {'MajorityClassSize': 119.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 11.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | datatrieve | [
"LOC6_0",
"LOC6_1",
"Added_LoC",
"Del_LoC",
"Diff_Block",
"Mod_Rate",
"Mod_Know",
"ReusedLoC"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,897 |
3,915 | predictive_accuracy | accuracy_score | kc3 | **Author**: Mike Chapman, NASA
**Source**: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/kc3.html) - 2004
**Please cite**: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, C... | {0: [0 - LOC_BLANK (numeric)],
1: [1 - BRANCH_COUNT (numeric)],
2: [2 - CALL_PAIRS (numeric)],
3: [3 - LOC_CODE_AND_COMMENT (numeric)],
4: [4 - LOC_COMMENTS (numeric)],
5: [5 - CONDITION_COUNT (numeric)],
6: [6 - CYCLOMATIC_COMPLEXITY (numeric)],
7: [7 - CYCLOMATIC_DENSITY (numeric)],
8: [8 - DECISION_COUNT (nu... | {'MajorityClassSize': 415.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 43.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 40.0,
'NumberOfInstances': 458.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 39.0,
'NumberOfSymbolicFeatures': 1.0,
'... | kc3 | [
"LOC_BLANK",
"BRANCH_COUNT",
"CALL_PAIRS",
"LOC_CODE_AND_COMMENT",
"LOC_COMMENTS",
"CONDITION_COUNT",
"CYCLOMATIC_COMPLEXITY",
"CYCLOMATIC_DENSITY",
"DECISION_COUNT",
"DECISION_DENSITY",
"DESIGN_COMPLEXITY",
"DESIGN_DENSITY",
"EDGE_COUNT",
"ESSENTIAL_COMPLEXITY",
"ESSENTIAL_DENSITY",
"... | [
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3,890 | predictive_accuracy | accuracy_score | ada_prior | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch)
Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php
Modified by TunedIT (converted to ARFF format)
ADA is the marketing database
The ta... | {0: [0 - age (numeric)],
1: [1 - workclass (nominal)],
2: [2 - fnlwgt (numeric)],
3: [3 - education (nominal)],
4: [4 - educationNum (numeric)],
5: [5 - maritalStatus (nominal)],
6: [6 - occupation (nominal)],
7: [7 - relationship (nominal)],
8: [8 - race (nominal)],
9: [9 - sex (nominal)],
10: [10 - capitalG... | {'MajorityClassSize': 3430.0,
'MaxNominalAttDistinctValues': 39.0,
'MinorityClassSize': 1132.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 4562.0,
'NumberOfInstancesWithMissingValues': 88.0,
'NumberOfMissingValues': 88.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 9... | ada_prior | [
"age",
"workclass",
"fnlwgt",
"education",
"educationNum",
"maritalStatus",
"occupation",
"relationship",
"race",
"sex",
"capitalGain",
"capitalLoss",
"hoursPerWeek",
"nativeCountry"
] | [
false,
true,
false,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
true
] | 1,899 |
146,815 | 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': 3707.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 75.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1101.0,
'NumberOfInstances': 3782.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1079.0,
'NumberOfSymbolicFeatures': 2... | 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,
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false,
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false... | 1,900 |
3,916 | predictive_accuracy | accuracy_score | kc1-binary | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This is a PROMISE Software Engineering Repository data set made publicly
available in order to encourage repeatable, verifiable, refutable, and/or
improvable predictive mode... | {0: [0 - PERCENT_PUB_DATA (numeric)],
1: [1 - ACCESS_TO_PUB_DATA (numeric)],
2: [2 - COUPLING_BETWEEN_OBJECTS (numeric)],
3: [3 - DEPTH (numeric)],
4: [4 - LACK_OF_COHESION_OF_METHODS (numeric)],
5: [5 - NUM_OF_CHILDREN (numeric)],
6: [6 - DEP_ON_CHILD (numeric)],
7: [7 - FAN_IN (numeric)],
8: [8 - RESPONSE_FOR... | {'MajorityClassSize': 85.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 60.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 95.0,
'NumberOfInstances': 145.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 94.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | kc1-binary | [
"PERCENT_PUB_DATA",
"ACCESS_TO_PUB_DATA",
"COUPLING_BETWEEN_OBJECTS",
"DEPTH",
"LACK_OF_COHESION_OF_METHODS",
"NUM_OF_CHILDREN",
"DEP_ON_CHILD",
"FAN_IN",
"RESPONSE_FOR_CLASS",
"WEIGHTED_METHODS_PER_CLASS",
"minLOC_BLANK",
"minBRANCH_COUNT",
"minLOC_CODE_AND_COMMENT",
"minLOC_COMMENTS",
... | [
false,
false,
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f... | 1,901 |
3,937 | predictive_accuracy | accuracy_score | PopularKids | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Datasets of Data And Story Library, project illustrating use of basic statistic methods, converted to arff format by Hakan Kjellerstrand.
Source: TunedIT: http://tunedit.org/repo/DASL
DASL file http://lib.stat.cmu.edu/DASL/Datafiles/PopularKids.h... | {0: [0 - Gender (nominal)],
1: [1 - Grade (numeric)],
2: [2 - Age (numeric)],
3: [3 - Race (nominal)],
4: [4 - Urban/Rural (nominal)],
5: [5 - School (nominal)],
6: [6 - Goals (nominal)],
7: [7 - Grades (numeric)],
8: [8 - Sports (numeric)],
9: [9 - Looks (numeric)],
10: [10 - Money (numeric)]} | {'MajorityClassSize': 247.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 90.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 478.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 5.0,
'c... | PopularKids | [
"Gender",
"Grade",
"Age",
"Race",
"Urban/Rural",
"School",
"Grades",
"Sports",
"Looks",
"Money"
] | [
true,
false,
false,
true,
true,
true,
false,
false,
false,
false
] | 1,902 |
3,880 | predictive_accuracy | accuracy_score | arrhythmia | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - height (numeric)],
3: [3 - weight (numeric)],
4: [4 - QRSduration (numeric)],
5: [5 - PRinterval (numeric)],
6: [6 - Q-Tinterval (numeric)],
7: [7 - Tinterval (numeric)],
8: [8 - Pinterval (numeric)],
9: [9 - QRS (numeric)],
10: [10 - T (numeric)],
11:... | {'MajorityClassSize': 245.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 207.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 280.0,
'NumberOfInstances': 452.0,
'NumberOfInstancesWithMissingValues': 384.0,
'NumberOfMissingValues': 408.0,
'NumberOfNumericFeatures': 206.0,
'NumberOfSymbolicFeatures': ... | arrhythmia | [
"age",
"sex",
"height",
"weight",
"QRSduration",
"PRinterval",
"Q-Tinterval",
"Tinterval",
"Pinterval",
"QRS",
"T",
"P",
"QRST",
"J",
"heartrate",
"chDI_Qwave",
"chDI_Rwave",
"chDI_Swave",
"chDI_RPwave",
"chDI_SPwave",
"chDI_intrinsicReflecttions",
"chDI_RRwaveExists",
"c... | [
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tr... | 1,903 |
3,920 | predictive_accuracy | accuracy_score | mw1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
%-*- text -*-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This is a PROMISE data set made publicly available in order to encourage
repeatable, verifiable, refutable, and/or improvable predictive models
of software eng... | {0: [0 - LOC_BLANK (numeric)],
1: [1 - BRANCH_COUNT (numeric)],
2: [2 - CALL_PAIRS (numeric)],
3: [3 - LOC_CODE_AND_COMMENT (numeric)],
4: [4 - LOC_COMMENTS (numeric)],
5: [5 - CONDITION_COUNT (numeric)],
6: [6 - CYCLOMATIC_COMPLEXITY (numeric)],
7: [7 - CYCLOMATIC_DENSITY (numeric)],
8: [8 - DECISION_COUNT (nu... | {'MajorityClassSize': 372.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 31.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 38.0,
'NumberOfInstances': 403.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 37.0,
'NumberOfSymbolicFeatures': 1.0,
'... | mw1 | [
"LOC_BLANK",
"BRANCH_COUNT",
"CALL_PAIRS",
"LOC_CODE_AND_COMMENT",
"LOC_COMMENTS",
"CONDITION_COUNT",
"CYCLOMATIC_COMPLEXITY",
"CYCLOMATIC_DENSITY",
"DECISION_COUNT",
"DECISION_DENSITY",
"DESIGN_COMPLEXITY",
"DESIGN_DENSITY",
"EDGE_COUNT",
"ESSENTIAL_COMPLEXITY",
"ESSENTIAL_DENSITY",
"... | [
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f... | 1,904 |
146,896 | 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': 3707.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 75.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1101.0,
'NumberOfInstances': 3782.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1079.0,
'NumberOfSymbolicFeatures': 2... | 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",
... | [
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false... | 1,905 |
3,951 | predictive_accuracy | accuracy_score | desharnais | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Dataset from the MLRR repository: http://axon.cs.byu.edu:5000/ | {0: [0 - ID (numeric)],
1: [1 - Project (numeric)],
2: [2 - TeamExp (numeric)],
3: [3 - ManagerExp (numeric)],
4: [4 - YearEnd (numeric)],
5: [5 - Length (numeric)],
6: [6 - Effort (numeric)],
7: [7 - Transactions (numeric)],
8: [8 - Entities (numeric)],
9: [9 - PointsNonAdjust (numeric)],
10: [10 - Adjustmen... | {'MajorityClassSize': 46.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 10.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 81.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | desharnais | [
"Project",
"TeamExp",
"ManagerExp",
"YearEnd",
"Length",
"Effort",
"Transactions",
"Entities",
"PointsNonAdjust",
"Adjustment",
"PointsAjust"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,906 |
3,886 | predictive_accuracy | accuracy_score | soybean | **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 - date (nominal)],
1: [1 - plant-stand (nominal)],
2: [2 - precip (nominal)],
3: [3 - temp (nominal)],
4: [4 - hail (nominal)],
5: [5 - crop-hist (nominal)],
6: [6 - area-damaged (nominal)],
7: [7 - severity (nominal)],
8: [8 - seed-tmt (nominal)],
9: [9 - germination (nominal)],
10: [10 - plant-growth... | {'MajorityClassSize': 591.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 92.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 36.0,
'NumberOfInstances': 683.0,
'NumberOfInstancesWithMissingValues': 121.0,
'NumberOfMissingValues': 2337.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 36.... | soybean | [
"date",
"plant-stand",
"precip",
"temp",
"hail",
"crop-hist",
"area-damaged",
"severity",
"seed-tmt",
"germination",
"plant-growth",
"leaves",
"leafspots-halo",
"leafspots-marg",
"leafspot-size",
"leaf-shread",
"leaf-malf",
"leaf-mild",
"stem",
"lodging",
"stem-cankers",
"c... | [
true,
true,
true,
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true,
true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,907 |
3,955 | predictive_accuracy | accuracy_score | badges2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Dataset from the MLRR repository: http://axon.cs.byu.edu:5000/ | {0: [0 - ID (numeric)],
1: [1 - length (numeric)],
2: [2 - even_odd (nominal)],
3: [3 - first_char_vowel (nominal)],
4: [4 - second_char_vowel (nominal)],
5: [5 - vowels (numeric)],
6: [6 - consonants (numeric)],
7: [7 - vowel_consonant_ratio (numeric)],
8: [8 - spaces (numeric)],
9: [9 - dots (numeric)],
10:... | {'MajorityClassSize': 210.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 84.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 294.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 4.0,
'c... | badges2 | [
"length",
"even_odd",
"first_char_vowel",
"second_char_vowel",
"vowels",
"consonants",
"vowel_consonant_ratio",
"spaces",
"dots",
"words"
] | [
false,
true,
true,
true,
false,
false,
false,
false,
false,
false
] | 1,908 |
3,883 | predictive_accuracy | accuracy_score | mfeat-karhunen | **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': 65.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 64.0,
'NumberOfSymbolicFeatures': 1.0,... | mfeat-karhunen | [
"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... | [
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f... | 1,909 |
3,949 | predictive_accuracy | accuracy_score | teachingAssistant | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Dataset from the MLRR repository: http://axon.cs.byu.edu:5000/ | {0: [0 - ID (numeric)],
1: [1 - EnglishSepaker (nominal)],
2: [2 - courseInstructor (nominal)],
3: [3 - course (nominal)],
4: [4 - summer (nominal)],
5: [5 - classSize (numeric)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 52.0,
'MaxNominalAttDistinctValues': 26.0,
'MinorityClassSize': 49.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 151.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 5.0,
'co... | teachingAssistant | [
"ID",
"EnglishSepaker",
"courseInstructor",
"course",
"summer",
"classSize"
] | [
false,
true,
true,
true,
true,
false
] | 1,910 |
3,843 | predictive_accuracy | accuracy_score | optdigits | **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 - input1 (numeric)],
1: [1 - input2 (numeric)],
2: [2 - input3 (numeric)],
3: [3 - input4 (numeric)],
4: [4 - input5 (numeric)],
5: [5 - input6 (numeric)],
6: [6 - input7 (numeric)],
7: [7 - input8 (numeric)],
8: [8 - input9 (numeric)],
9: [9 - input10 (numeric)],
10: [10 - input11 (numeric)],
11: [11... | {'MajorityClassSize': 5048.0,
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'NumberOfSymbolicFeatures': 1.0,... | optdigits | [
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3,865 | predictive_accuracy | accuracy_score | ipums_la_98-small | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (numeric)],
6: [6 - rent (numeric)],
7: [7 - ftotinc (numeric)],
8: [8 - nfams (nominal)],
9: [9 - ncouples (nominal)],
10: [10 - nmothers (nominal)],
11: [11 - nfa... | {'MajorityClassSize': 6694.0,
'MaxNominalAttDistinctValues': 15.0,
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'NumberOfInstances': 7485.0,
'NumberOfInstancesWithMissingValues': 7369.0,
'NumberOfMissingValues': 32427.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeature... | ipums_la_98-small | [
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3,939 | predictive_accuracy | accuracy_score | lymphoma_9classes | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - GENE1835X (numeric)],
1: [1 - GENE1836X (numeric)],
2: [2 - GENE1865X (numeric)],
3: [3 - GENE1380X (numeric)],
4: [4 - GENE1933X (numeric)],
5: [5 - GENE1932X (numeric)],
6: [6 - GENE1931X (numeric)],
7: [7 - GENE1930X (numeric)],
8: [8 - GENE3129X (numeric)],
9: [9 - GENE3126X (numeric)],
10: [10 -... | {'MajorityClassSize': 46.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 4027.0,
'NumberOfInstances': 96.0,
'NumberOfInstancesWithMissingValues': 89.0,
'NumberOfMissingValues': 19667.0,
'NumberOfNumericFeatures': 4026.0,
'NumberOfSymbolicFeatures': 1... | lymphoma_9classes | [
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f... | 1,915 |
211,815 | 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,
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'NumberOfClasses': nan,
'NumberOfFeatures': 1101.0,
'NumberOfInstances': 3782.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1079.0,
'NumberOfSymbolicFeatures': 22.0,... | slashdot | [
"Entertainment",
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"Main",
"Developers",
"Apache",
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"Mobile",
"Science",
"IT",
"BSD",
"Idle",
"Games",
"YourRightsOnline",
"AskSlashdot",
"Apple",
"BookReviews",
"Hardware",
"Meta",
"Linux",
"Politics",
"Technology",
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... | [
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3,938 | predictive_accuracy | accuracy_score | lymphoma_2classes | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - GENE1835X (numeric)],
1: [1 - GENE1836X (numeric)],
2: [2 - GENE1865X (numeric)],
3: [3 - GENE1380X (numeric)],
4: [4 - GENE1933X (numeric)],
5: [5 - GENE1932X (numeric)],
6: [6 - GENE1931X (numeric)],
7: [7 - GENE1930X (numeric)],
8: [8 - GENE3129X (numeric)],
9: [9 - GENE3126X (numeric)],
10: [10 -... | {'MajorityClassSize': 23.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 22.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4027.0,
'NumberOfInstances': 45.0,
'NumberOfInstancesWithMissingValues': 38.0,
'NumberOfMissingValues': 5948.0,
'NumberOfNumericFeatures': 4026.0,
'NumberOfSymbolicFeatures': 1... | lymphoma_2classes | [
"GENE1835X",
"GENE1836X",
"GENE1865X",
"GENE1380X",
"GENE1933X",
"GENE1932X",
"GENE1931X",
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f... | 1,917 |
3,858 | predictive_accuracy | accuracy_score | mfeat-zernike | **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': 48.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 47.0,
'NumberOfSymbolicFeatures': 1.0,... | mfeat-zernike | [
"att1",
"att2",
"att3",
"att4",
"att5",
"att6",
"att7",
"att8",
"att9",
"att10",
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"att20",
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"att25",
"att26",
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"att29",
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f... | 1,918 |
3,919 | predictive_accuracy | accuracy_score | pc2 | **Author**: Mike Chapman, NASA
**Source**: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/pc2.html) - 2004
**Please cite**: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, C... | {0: [0 - BRANCH_COUNT (numeric)],
1: [1 - CALL_PAIRS (numeric)],
2: [2 - LOC_CODE_AND_COMMENT (numeric)],
3: [3 - LOC_COMMENTS (numeric)],
4: [4 - CONDITION_COUNT (numeric)],
5: [5 - CYCLOMATIC_COMPLEXITY (numeric)],
6: [6 - CYCLOMATIC_DENSITY (numeric)],
7: [7 - DECISION_COUNT (numeric)],
8: [8 - DECISION_DENS... | {'MajorityClassSize': 5566.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 23.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 5589.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 36.0,
'NumberOfSymbolicFeatures': 1.0,
... | pc2 | [
"BRANCH_COUNT",
"CALL_PAIRS",
"LOC_CODE_AND_COMMENT",
"LOC_COMMENTS",
"CONDITION_COUNT",
"CYCLOMATIC_COMPLEXITY",
"CYCLOMATIC_DENSITY",
"DECISION_COUNT",
"DECISION_DENSITY",
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"ESSENTIAL_COMPLEXITY",
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f... | 1,919 |
3,943 | predictive_accuracy | accuracy_score | lymphoma_11classes | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - GENE1835X (numeric)],
1: [1 - GENE1836X (numeric)],
2: [2 - GENE1865X (numeric)],
3: [3 - GENE1380X (numeric)],
4: [4 - GENE1933X (numeric)],
5: [5 - GENE1932X (numeric)],
6: [6 - GENE1931X (numeric)],
7: [7 - GENE1930X (numeric)],
8: [8 - GENE3129X (numeric)],
9: [9 - GENE3126X (numeric)],
10: [10 -... | {'MajorityClassSize': 23.0,
'MaxNominalAttDistinctValues': 11.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 4027.0,
'NumberOfInstances': 96.0,
'NumberOfInstancesWithMissingValues': 89.0,
'NumberOfMissingValues': 19667.0,
'NumberOfNumericFeatures': 4026.0,
'NumberOfSymbolicFeatures':... | lymphoma_11classes | [
"GENE1835X",
"GENE1836X",
"GENE1865X",
"GENE1380X",
"GENE1933X",
"GENE1932X",
"GENE1931X",
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f... | 1,921 |
4,001 | predictive_accuracy | accuracy_score | pc1_req | **Author**:
**Source**: Unknown - Date unknown
**Please cite**: | {0: [0 - ACTION (numeric)],
1: [1 - CONDITIONAL (numeric)],
2: [2 - CONTINUANCE (numeric)],
3: [3 - IMPERATIVE (numeric)],
4: [4 - OPTION (nominal)],
5: [5 - RISK_LEVEL (numeric)],
6: [6 - SOURCE (numeric)],
7: [7 - WEAK_PHRASE (numeric)],
8: [8 - DEFECT (nominal)]} | {'MajorityClassSize': 213.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 107.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 320.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | pc1_req | [
"ACTION",
"CONDITIONAL",
"CONTINUANCE",
"IMPERATIVE",
"OPTION",
"RISK_LEVEL",
"SOURCE",
"WEAK_PHRASE"
] | [
false,
false,
false,
false,
true,
false,
false,
false
] | 1,922 |
3,856 | predictive_accuracy | accuracy_score | kdd_ipums_la_97-small | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (numeric)],
6: [6 - rent (numeric)],
7: [7 - ftotinc (numeric)],
8: [8 - nfams (numeric)],
9: [9 - ncouples (numeric)],
10: [10 - nmothers (numeric)],
11: [11 - nfa... | {'MajorityClassSize': 4425.0,
'MaxNominalAttDistinctValues': 191.0,
'MinorityClassSize': 2594.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 7019.0,
'NumberOfInstancesWithMissingValues': 7019.0,
'NumberOfMissingValues': 43814.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatu... | kdd_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... | [
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4,199 | predictive_accuracy | accuracy_score | monks-problems-3 | **Author**: Sebastian Thrun (Carnegie Mellon University)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/MONK's+Problems) - October 1992
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**The Monk's Problems: Problem 3**
Once upon a time, in July 1991, the monks of Corsend... | {0: [0 - class (nominal)],
1: [1 - attr1 (nominal)],
2: [2 - attr2 (nominal)],
3: [3 - attr3 (nominal)],
4: [4 - attr4 (nominal)],
5: [5 - attr5 (nominal)],
6: [6 - attr6 (nominal)]} | {'MajorityClassSize': 288.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 266.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 554.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | monks-problems-3 | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6"
] | [
true,
true,
true,
true,
true,
true
] | 1,924 |
3,881 | predictive_accuracy | accuracy_score | ipums_la_99-small | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converte... | {0: [0 - year (nominal)],
1: [1 - gq (nominal)],
2: [2 - gqtypeg (nominal)],
3: [3 - farm (nominal)],
4: [4 - ownershg (nominal)],
5: [5 - value (numeric)],
6: [6 - rent (numeric)],
7: [7 - ftotinc (numeric)],
8: [8 - nfams (nominal)],
9: [9 - ncouples (nominal)],
10: [10 - nmothers (nominal)],
11: [11 - nfa... | {'MajorityClassSize': 8276.0,
'MaxNominalAttDistinctValues': 17.0,
'MinorityClassSize': 568.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 57.0,
'NumberOfInstances': 8844.0,
'NumberOfInstancesWithMissingValues': 8844.0,
'NumberOfMissingValues': 34843.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeature... | 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... | [
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4,197 | predictive_accuracy | accuracy_score | monks-problems-1 | **Author**: Sebastian Thrun (Carnegie Mellon University)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/MONK's+Problems) - October 1992
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**The Monk's Problems: Problem 1**
Once upon a time, in July 1991, the monks of Corsend... | {0: [0 - class (nominal)],
1: [1 - attr1 (nominal)],
2: [2 - attr2 (nominal)],
3: [3 - attr3 (nominal)],
4: [4 - attr4 (nominal)],
5: [5 - attr5 (nominal)],
6: [6 - attr6 (nominal)]} | {'MajorityClassSize': 278.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 278.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 556.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | monks-problems-1 | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6"
] | [
true,
true,
true,
true,
true,
true
] | 1,926 |
4,205 | predictive_accuracy | accuracy_score | squash-unstored | **Author**: Winna Harvey
**Source**: [original](http://www.cs.waikato.ac.nz/ml/weka/datasets.html) -
**Please cite**:
Squash Harvest Unstored
Data source: Winna Harvey
Crop and Food Research, Christchurch, New Zealand
The purpose of the research was to determine the changes taking place in squash fruit durin... | {0: [0 - site (nominal)],
1: [1 - daf (nominal)],
2: [2 - fruit (nominal)],
3: [3 - weight (numeric)],
4: [4 - pene (numeric)],
5: [5 - solids (numeric)],
6: [6 - brix (numeric)],
7: [7 - a* (numeric)],
8: [8 - egdd (numeric)],
9: [9 - fgdd (numeric)],
10: [10 - groundspot_a* (numeric)],
11: [11 - glucose (n... | {'MajorityClassSize': 24.0,
'MaxNominalAttDistinctValues': 22.0,
'MinorityClassSize': 4.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 24.0,
'NumberOfInstances': 52.0,
'NumberOfInstancesWithMissingValues': 9.0,
'NumberOfMissingValues': 39.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 4.0,
'c... | squash-unstored | [
"site",
"daf",
"fruit",
"weight",
"pene",
"solids",
"brix",
"a*",
"egdd",
"fgdd",
"groundspot_a*",
"glucose",
"fructose",
"sucrose",
"total",
"glucose+fructose",
"starch",
"sweetness",
"flavour",
"dry/moist",
"fibre",
"heat_input_emerg",
"heat_input_flower"
] | [
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false,
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false,
false,
false,
false,
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] | 1,927 |
4,198 | predictive_accuracy | accuracy_score | monks-problems-2 | **Author**: Sebastian Thrun (Carnegie Mellon University)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/MONK's+Problems) - October 1992
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**The Monk's Problems: Problem 2**
Once upon a time, in July 1991, the monks of Corsend... | {0: [0 - class (nominal)],
1: [1 - attr1 (nominal)],
2: [2 - attr2 (nominal)],
3: [3 - attr3 (nominal)],
4: [4 - attr4 (nominal)],
5: [5 - attr5 (nominal)],
6: [6 - attr6 (nominal)]} | {'MajorityClassSize': 395.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 206.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 601.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'c... | monks-problems-2 | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6"
] | [
true,
true,
true,
true,
true,
true
] | 1,929 |
4,203 | predictive_accuracy | accuracy_score | pasture | **Author**: Dave Barker
**Source**: [original](http://www.cs.waikato.ac.nz/ml/weka/datasets.html) -
**Please cite**:
Pasture Production
Data source: Dave Barker
AgResearch Grasslands, Palmerston North, New Zealand
The objective was to predict pasture production from a variety of biophysical factors. Vegeta... | {0: [0 - fertiliser (nominal)],
1: [1 - slope (numeric)],
2: [2 - aspect-dev-NW (numeric)],
3: [3 - OlsenP (numeric)],
4: [4 - MinN (numeric)],
5: [5 - TS (numeric)],
6: [6 - Ca-Mg (numeric)],
7: [7 - LOM (numeric)],
8: [8 - NFIX-mean (numeric)],
9: [9 - Eworms-main-3 (numeric)],
10: [10 - Eworms-No-species (... | {'MajorityClassSize': 12.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 12.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 36.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | pasture | [
"fertiliser",
"slope",
"aspect-dev-NW",
"OlsenP",
"MinN",
"TS",
"Ca-Mg",
"LOM",
"NFIX-mean",
"Eworms-main-3",
"Eworms-No-species",
"KUnSat",
"OM",
"Air-Perm",
"Porosity",
"HFRG-pct-mean",
"legume-yield",
"OSPP-pct-mean",
"Jan-Mar-mean-TDR",
"Annual-Mean-Runoff",
"root-surface... | [
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] | 1,930 |
4,202 | predictive_accuracy | accuracy_score | grub-damage | **Author**: R. J. Townsend
**Source**: [original](http://www.cs.waikato.ac.nz/ml/weka/datasets.html) -
**Please cite**:
Grass Grubs and Damage Ranking
Data source: R. J. Townsend
AgResearch, Lincoln, New Zealand
Grass grubs are one of the major insect pests of pasture in Canterbury and can cause severe pa... | {0: [0 - year_zone (nominal)],
1: [1 - year (nominal)],
2: [2 - strip (numeric)],
3: [3 - pdk (numeric)],
4: [4 - damage_rankRJT (nominal)],
5: [5 - damage_rankALL (nominal)],
6: [6 - dry_or_irr (nominal)],
7: [7 - zone (nominal)],
8: [8 - GG_new (nominal)]} | {'MajorityClassSize': 49.0,
'MaxNominalAttDistinctValues': 21.0,
'MinorityClassSize': 19.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 155.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 7.0,
'co... | grub-damage | [
"year_zone",
"year",
"strip",
"pdk",
"damage_rankRJT",
"damage_rankALL",
"dry_or_irr",
"zone"
] | [
true,
true,
false,
false,
true,
true,
true,
true
] | 1,932 |
3,907 | predictive_accuracy | accuracy_score | mc1 | **Author**: Mike Chapman, NASA
**Source**: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/mc1.html) - 2004
**Please cite**: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, C... | {0: [0 - LOC_BLANK (numeric)],
1: [1 - BRANCH_COUNT (numeric)],
2: [2 - CALL_PAIRS (numeric)],
3: [3 - LOC_CODE_AND_COMMENT (numeric)],
4: [4 - LOC_COMMENTS (numeric)],
5: [5 - CONDITION_COUNT (numeric)],
6: [6 - CYCLOMATIC_COMPLEXITY (numeric)],
7: [7 - CYCLOMATIC_DENSITY (numeric)],
8: [8 - DECISION_COUNT (nu... | {'MajorityClassSize': 9398.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 68.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 9466.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 38.0,
'NumberOfSymbolicFeatures': 1.0,
... | mc1 | [
"LOC_BLANK",
"BRANCH_COUNT",
"CALL_PAIRS",
"LOC_CODE_AND_COMMENT",
"LOC_COMMENTS",
"CONDITION_COUNT",
"CYCLOMATIC_COMPLEXITY",
"CYCLOMATIC_DENSITY",
"DECISION_COUNT",
"DESIGN_COMPLEXITY",
"DESIGN_DENSITY",
"EDGE_COUNT",
"ESSENTIAL_COMPLEXITY",
"ESSENTIAL_DENSITY",
"LOC_EXECUTABLE",
"PA... | [
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f... | 1,933 |
4,196 | predictive_accuracy | accuracy_score | hayes-roth | **Author**: Barbara and Frederick Hayes-Roth
**Source**: [original](https://archive.ics.uci.edu/ml/datasets/Hayes-Roth) -
**Please cite**:
Hayes-Roth Database
This is a merged version of the separate train and test set which are usually distributed. On OpenML this train-test split can be found as one of th... | {0: [0 - hobby (numeric)],
1: [1 - age (numeric)],
2: [2 - educational_level (numeric)],
3: [3 - marital_status (numeric)],
4: [4 - class (nominal)]} | {'MajorityClassSize': 65.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 31.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 160.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | hayes-roth | [
"hobby",
"age",
"educational_level",
"marital_status"
] | [
false,
false,
false,
false
] | 1,935 |
4,208 | predictive_accuracy | accuracy_score | aids | **Author**: Jeffrey S. Simonoff
**Source**: [original](http://www.stern.nyu.edu/~jsimonof/AnalCatData) -
**Please cite**: Jeffrey S. Simonoff. Analyzing Categorical Data, Springer-Verlag, New York, 2003
Data originating from the book "Analyzing Categorical Data" by Jeffrey S. Simonoff. | {0: [0 - Sex (nominal)],
1: [1 - Age (nominal)],
2: [2 - Race (nominal)],
3: [3 - AIDS (numeric)],
4: [4 - Total (numeric)]} | {'MajorityClassSize': 25.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 25.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost... | aids | [
"Age",
"Race",
"AIDS",
"Total"
] | [
true,
true,
false,
false
] | 1,936 |
4,201 | predictive_accuracy | accuracy_score | SPECTF | **Author**: Krzysztof J. Cios","Lukasz A.
**Source**: [original](https://archive.ics.uci.edu/ml/datasets/SPECTF+Heart) -
**Please cite**:
SPECTF heart data
This is a merged version of the separate train and test set which are usually distributed. On OpenML this train-test split can be found as one of the poss... | {0: [0 - OVERALL_DIAGNOSIS (nominal)],
1: [1 - F1R (numeric)],
2: [2 - F1S (numeric)],
3: [3 - F2R (numeric)],
4: [4 - F2S (numeric)],
5: [5 - F3R (numeric)],
6: [6 - F3S (numeric)],
7: [7 - F4R (numeric)],
8: [8 - F4S (numeric)],
9: [9 - F5R (numeric)],
10: [10 - F5S (numeric)],
11: [11 - F6R (numeric)],
1... | {'MajorityClassSize': 254.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 95.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 45.0,
'NumberOfInstances': 349.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 44.0,
'NumberOfSymbolicFeatures': 1.0,
'... | SPECTF | [
"F1R",
"F1S",
"F2R",
"F2S",
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"F3S",
"F4R",
"F4S",
"F5R",
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"F16R",
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"F17... | [
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f... | 1,937 |
4,206 | predictive_accuracy | accuracy_score | white-clover | **Author**: Ian Tarbotton
**Source**: [original](http://www.cs.waikato.ac.nz/ml/weka/datasets.html) -
**Please cite**:
White Clover Persistence Trials
Data source: Ian Tarbotton
AgResearch, Whatawhata Research Centre, Hamilton, New Zealand
The objective was to determine the mechanisms which influence the p... | {0: [0 - strata (nominal)],
1: [1 - plot (nominal)],
2: [2 - paddock (nominal)],
3: [3 - WhiteClover-91 (numeric)],
4: [4 - BareGround-91 (numeric)],
5: [5 - Cocksfoot-91 (numeric)],
6: [6 - OtherGrasses-91 (numeric)],
7: [7 - OtherLegumes-91 (numeric)],
8: [8 - RyeGrass-91 (numeric)],
9: [9 - Weeds-91 (numeri... | {'MajorityClassSize': 38.0,
'MaxNominalAttDistinctValues': 7.0,
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'NumberOfClasses': 4.0,
'NumberOfFeatures': 32.0,
'NumberOfInstances': 63.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 5.0,
'cos... | white-clover | [
"strata",
"plot",
"paddock",
"WhiteClover-91",
"BareGround-91",
"Cocksfoot-91",
"OtherGrasses-91",
"OtherLegumes-91",
"RyeGrass-91",
"Weeds-91",
"WhiteClover-92",
"BareGround-92",
"Cocksfoot-92",
"OtherGrasses-92",
"OtherLegumes-92",
"RyeGrass-92",
"Weeds-92",
"WhiteClover-93",
"... | [
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] | 1,938 |
4,200 | predictive_accuracy | accuracy_score | SPECT | **Author**: Krzysztof J. Cios","Lukasz A.
**Source**: [original](https://archive.ics.uci.edu/ml/datasets/SPECT+Heart) -
**Please cite**:
SPECT heart data
This is a merged version of the separate train and test set which are usually distributed. On OpenML this train-test split can be found as one of the possib... | {0: [0 - OVERALL_DIAGNOSIS (nominal)],
1: [1 - F1 (nominal)],
2: [2 - F2 (nominal)],
3: [3 - F3 (nominal)],
4: [4 - F4 (nominal)],
5: [5 - F5 (nominal)],
6: [6 - F6 (nominal)],
7: [7 - F7 (nominal)],
8: [8 - F8 (nominal)],
9: [9 - F9 (nominal)],
10: [10 - F10 (nominal)],
11: [11 - F11 (nominal)],
12: [12 - ... | {'MajorityClassSize': 212.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 55.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 267.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 23.0,
'... | SPECT | [
"F1",
"F2",
"F3",
"F4",
"F5",
"F6",
"F7",
"F8",
"F9",
"F10",
"F11",
"F12",
"F13",
"F14",
"F15",
"F16",
"F17",
"F18",
"F19",
"F20",
"F21",
"F22"
] | [
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true,
true,
true,
true,
true,
true,
true
] | 1,939 |
3,940 | predictive_accuracy | accuracy_score | leukemia | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring.
Science, VOL 286, pp. 531-537, 15 October 1999.
Web supplement to the article
T.R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J... | {0: [0 - AFFX-BioB-5_at (numeric)],
1: [1 - AFFX-BioB-M_at (numeric)],
2: [2 - AFFX-BioB-3_at (numeric)],
3: [3 - AFFX-BioC-5_at (numeric)],
4: [4 - AFFX-BioC-3_at (numeric)],
5: [5 - AFFX-BioDn-5_at (numeric)],
6: [6 - AFFX-BioDn-3_at (numeric)],
7: [7 - AFFX-CreX-5_at (numeric)],
8: [8 - AFFX-CreX-3_at (numer... | {'MajorityClassSize': 47.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 25.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7130.0,
'NumberOfInstances': 72.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7129.0,
'NumberOfSymbolicFeatures': 1.0,
... | leukemia | [
"AFFX-BioB-5_at",
"AFFX-BioB-M_at",
"AFFX-BioB-3_at",
"AFFX-BioC-5_at",
"AFFX-BioC-3_at",
"AFFX-BioDn-5_at",
"AFFX-BioDn-3_at",
"AFFX-CreX-5_at",
"AFFX-CreX-3_at",
"AFFX-BioB-5_st",
"AFFX-BioB-M_st",
"AFFX-BioB-3_st",
"AFFX-BioC-5_st",
"AFFX-BioC-3_st",
"AFFX-BioDn-5_st",
"AFFX-BioDn-3... | [
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f... | 1,940 |
4,242 | predictive_accuracy | accuracy_score | analcatdata_broadway | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Show (nominal)],
1: [1 - Type (nominal)],
2: [2 - Revival (nominal)],
3: [3 - NYT_rating (numeric)],
4: [4 - DN_rating (numeric)],
5: [5 - Tony_awards (nominal)],
6: [6 - Tony_nominations (nominal)],
7: [7 - Week_1_attendance (numeric)],
8: [8 - Show_run (nominal)],
9: [9 - Show_run_code (nominal)]} | {'MajorityClassSize': 68.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 95.0,
'NumberOfInstancesWithMissingValues': 6.0,
'NumberOfMissingValues': 9.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
'cost_... | analcatdata_broadway | [
"Type",
"Revival",
"NYT_rating",
"DN_rating",
"Tony_awards",
"Tony_nominations",
"Week_1_attendance"
] | [
true,
true,
false,
false,
true,
true,
false
] | 1,941 |
3,942 | predictive_accuracy | accuracy_score | tumors_C | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Embryonal tumours of the central nervous system
Prediction of Central Nervous System Embryonal Tumour Outcome based on Gene Expression.
Nature, VOL 415, pp. 436-442, 24 January 2002.
Scott L. Pomeroy, Pablo Tamayo, Michelle Gaasenbeek, Lisa M. S... | {0: [0 - AFFX-BioB-5_at (numeric)],
1: [1 - AFFX-BioB-M_at (numeric)],
2: [2 - AFFX-BioB-3_at (numeric)],
3: [3 - AFFX-BioC-5_at (numeric)],
4: [4 - AFFX-BioC-3_at (numeric)],
5: [5 - AFFX-BioDn-5_at (numeric)],
6: [6 - AFFX-BioDn-3_at (numeric)],
7: [7 - AFFX-CreX-5_at (numeric)],
8: [8 - AFFX-CreX-3_at (numer... | {'MajorityClassSize': 39.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 21.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7130.0,
'NumberOfInstances': 60.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7129.0,
'NumberOfSymbolicFeatures': 1.0,
... | tumors_C | [
"AFFX-BioB-5_at",
"AFFX-BioB-M_at",
"AFFX-BioB-3_at",
"AFFX-BioC-5_at",
"AFFX-BioC-3_at",
"AFFX-BioDn-5_at",
"AFFX-BioDn-3_at",
"AFFX-CreX-5_at",
"AFFX-CreX-3_at",
"AFFX-BioB-5_st",
"AFFX-BioB-M_st",
"AFFX-BioB-3_st",
"AFFX-BioC-5_st",
"AFFX-BioC-3_st",
"AFFX-BioDn-5_st",
"AFFX-BioDn-3... | [
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f... | 1,942 |
4,243 | predictive_accuracy | accuracy_score | analcatdata_boxing2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Judge (nominal)],
1: [1 - Official (nominal)],
2: [2 - Round (nominal)],
3: [3 - Winner (nominal)]} | {'MajorityClassSize': 71.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 61.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 132.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 4.0,
'co... | analcatdata_boxing2 | [
"Judge",
"Official",
"Round"
] | [
true,
true,
true
] | 1,943 |
4,244 | predictive_accuracy | accuracy_score | prnn_crabs | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Datasets for `Pattern Recognition and Neural Networks' by B.D. Ripley
=====================================================================
Cambridge University Press (1996) ISBN 0-521-46086-7
The background to the datasets is described in sec... | {0: [0 - sp (nominal)],
1: [1 - sex (nominal)],
2: [2 - index (numeric)],
3: [3 - FL (numeric)],
4: [4 - RW (numeric)],
5: [5 - CL (numeric)],
6: [6 - CW (numeric)],
7: [7 - BD (numeric)]} | {'MajorityClassSize': 100.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 100.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 200.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | prnn_crabs | [
"sex",
"index",
"FL",
"RW",
"CL",
"CW",
"BD"
] | [
true,
false,
false,
false,
false,
false,
false
] | 1,944 |
4,250 | predictive_accuracy | accuracy_score | analcatdata_bondrate | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - City (nominal)],
1: [1 - Population (numeric)],
2: [2 - Per_capita_income (numeric)],
3: [3 - Household_income (numeric)],
4: [4 - Discretionary_income (numeric)],
5: [5 - Publics_in_top_10 (nominal)],
6: [6 - Nonprofits_in_top_10 (nominal)],
7: [7 - For_profits_in_top_10 (nominal)],
8: [8 - Utilities_... | {'MajorityClassSize': 33.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 57.0,
'NumberOfInstancesWithMissingValues': 1.0,
'NumberOfMissingValues': 1.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 7.0,
'cos... | analcatdata_bondrate | [
"Population",
"Per_capita_income",
"Household_income",
"Discretionary_income",
"Publics_in_top_10",
"Nonprofits_in_top_10",
"For_profits_in_top_10",
"Utilities_in_top_10",
"Region",
"State_capital"
] | [
false,
false,
false,
false,
true,
true,
true,
true,
true,
true
] | 1,945 |
4,247 | predictive_accuracy | accuracy_score | analcatdata_lawsuit | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Length.of.service (numeric)],
1: [1 - CAP (numeric)],
2: [2 - PA.normalized (numeric)],
3: [3 - Minority (nominal)],
4: [4 - Laid.off (nominal)]} | {'MajorityClassSize': 245.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 19.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 264.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | analcatdata_lawsuit | [
"Length.of.service",
"CAP",
"PA.normalized",
"Minority"
] | [
false,
false,
false,
true
] | 1,947 |
4,245 | predictive_accuracy | accuracy_score | analcatdata_boxing1 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Judge (nominal)],
1: [1 - Official (nominal)],
2: [2 - Round (nominal)],
3: [3 - Winner (nominal)]} | {'MajorityClassSize': 78.0,
'MaxNominalAttDistinctValues': 12.0,
'MinorityClassSize': 42.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 120.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 4.0,
'co... | analcatdata_boxing1 | [
"Judge",
"Official",
"Round"
] | [
true,
true,
true
] | 1,948 |
4,248 | predictive_accuracy | accuracy_score | irish | **Author**: Vincent Greaney, Thomas Kelleghan (St. Patrick's College, Dublin)
**Source**: [StatLib](http://lib.stat.cmu.edu/datasets/irish.ed) - 1984
**Please cite**: [StatLib](http://lib.stat.cmu.edu/datasets/)
**Irish Educational Transitions Data**
Data on educational transitions for a sample of 500 Irish sch... | {0: [0 - Sex (nominal)],
1: [1 - DVRT (numeric)],
2: [2 - Educational_level (nominal)],
3: [3 - Leaving_Certificate (nominal)],
4: [4 - Prestige_score (numeric)],
5: [5 - Type_school (nominal)]} | {'MajorityClassSize': 278.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 222.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 32.0,
'NumberOfMissingValues': 32.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 4.0,
... | irish | [
"Sex",
"DVRT",
"Educational_level",
"Prestige_score",
"Type_school"
] | [
true,
false,
true,
false,
true
] | 1,949 |
4,213 | predictive_accuracy | accuracy_score | UNIX_user_data | **Author**: Terran Lane (terran@ecn.purdue.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/UNIX+User+Data) - Date unknown
**Please cite**:
This file contains 9 sets of sanitized user data drawn from the command histories of 8 UNIX computer users at Purdue over the course of up to 2 years (USER0 ... | {0: [0 - history (numeric)], 1: [1 - session (string)], 2: [2 - user (nominal)]} | {'MajorityClassSize': 2425.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 484.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 9100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 1.0,
... | UNIX_user_data | [
"history",
"session"
] | [
false,
false
] | 1,950 |
4,253 | predictive_accuracy | accuracy_score | prnn_cushings | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Datasets for `Pattern Recognition and Neural Networks' by B.D. Ripley
=====================================================================
Cambridge University Press (1996) ISBN 0-521-46086-7
The background to the datasets is described in sec... | {0: [0 - Label (nominal)],
1: [1 - Tetrahydrocortisone (numeric)],
2: [2 - Pregnanetriol (numeric)],
3: [3 - Type (nominal)]} | {'MajorityClassSize': 12.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 27.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cost_... | prnn_cushings | [
"Tetrahydrocortisone",
"Pregnanetriol"
] | [
false,
false
] | 1,951 |
4,255 | predictive_accuracy | accuracy_score | analcatdata_asbestos | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Task (nominal)],
1: [1 - Ventilation (nominal)],
2: [2 - Duration (numeric)],
3: [3 - Exposure (nominal)]} | {'MajorityClassSize': 46.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 37.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 83.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 3.0,
'cost... | analcatdata_asbestos | [
"Ventilation",
"Duration",
"Exposure"
] | [
true,
false,
true
] | 1,952 |
4,263 | predictive_accuracy | accuracy_score | analcatdata_japansolvent | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Firm (nominal)],
1: [1 - Solvent (nominal)],
2: [2 - EBIT/TA (numeric)],
3: [3 - NI/TC (numeric)],
4: [4 - Sales/TA (numeric)],
5: [5 - EBIT/Sales (numeric)],
6: [6 - NI/Sales (numeric)],
7: [7 - WC/TA (numeric)],
8: [8 - Equity/TL (numeric)],
9: [9 - Equity/TA (numeric)]} | {'MajorityClassSize': 27.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 25.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 52.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | analcatdata_japansolvent | [
"EBIT/TA",
"NI/TC",
"Sales/TA",
"EBIT/Sales",
"NI/Sales",
"WC/TA",
"Equity/TL",
"Equity/TA"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,953 |
4,249 | predictive_accuracy | accuracy_score | analcatdata_broadwaymult | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Show (nominal)],
1: [1 - Type (nominal)],
2: [2 - Revival (nominal)],
3: [3 - NYT_rating (numeric)],
4: [4 - DN_rating (numeric)],
5: [5 - Week_1_attendance (numeric)],
6: [6 - Award (nominal)],
7: [7 - Count (nominal)]} | {'MajorityClassSize': 118.0,
'MaxNominalAttDistinctValues': 95.0,
'MinorityClassSize': 21.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 285.0,
'NumberOfInstancesWithMissingValues': 18.0,
'NumberOfMissingValues': 27.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
... | analcatdata_broadwaymult | [
"Show",
"Type",
"Revival",
"NYT_rating",
"DN_rating",
"Week_1_attendance",
"Award"
] | [
true,
true,
true,
false,
false,
false,
true
] | 1,954 |
4,256 | predictive_accuracy | accuracy_score | analcatdata_reviewer | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Film (nominal)],
1: [1 - Roger_Ebert (nominal)],
2: [2 - Jeffrey_Lyons (nominal)],
3: [3 - Michael_Medved (nominal)],
4: [4 - Rex_Reed (nominal)],
5: [5 - Gene_Shalit (nominal)],
6: [6 - Joel_Siegel (nominal)],
7: [7 - Gene_Siskel (nominal)],
8: [8 - Peter_Travers (nominal)]} | {'MajorityClassSize': 141.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 54.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 379.0,
'NumberOfInstancesWithMissingValues': 365.0,
'NumberOfMissingValues': 1418.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 8.0,... | analcatdata_reviewer | [
"Jeffrey_Lyons",
"Michael_Medved",
"Rex_Reed",
"Gene_Shalit",
"Joel_Siegel",
"Gene_Siskel",
"Peter_Travers"
] | [
true,
true,
true,
true,
true,
true,
true
] | 1,955 |
4,252 | predictive_accuracy | accuracy_score | cars | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Committee on Statistical Graphics of the American Statistical
Association (ASA) invites you to participate in its Second (1983)
Exposition of Statistical Graphics Technology. The purposes of the
Exposition are (l) to provide a forum in which u... | {0: [0 - name (nominal)],
1: [1 - mpg (numeric)],
2: [2 - cylinders (nominal)],
3: [3 - displacement (numeric)],
4: [4 - horsepower (numeric)],
5: [5 - weight (numeric)],
6: [6 - acceleration (numeric)],
7: [7 - model.year (numeric)],
8: [8 - origin (nominal)]} | {'MajorityClassSize': 254.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 73.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 406.0,
'NumberOfInstancesWithMissingValues': 14.0,
'NumberOfMissingValues': 14.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 2.0,
'... | cars | [
"mpg",
"cylinders",
"displacement",
"horsepower",
"weight",
"acceleration",
"model.year"
] | [
false,
true,
false,
false,
false,
false,
false
] | 1,956 |
4,262 | predictive_accuracy | accuracy_score | schizo | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Schizophrenic Eye-Tracking Data in Rubin and Wu (1997)
Biometrics. Yingnian Wu (wu@hustat.harvard.edu) [14/Oct/97]
Information about the dataset
CLASSTYPE: nominal
CLASSINDEX: last | {0: [0 - ID (numeric)],
1: [1 - target (nominal)],
2: [2 - gain_ratio_1 (numeric)],
3: [3 - gain_ratio_2 (numeric)],
4: [4 - gain_ratio_3 (numeric)],
5: [5 - gain_ratio_4 (numeric)],
6: [6 - gain_ratio_5 (numeric)],
7: [7 - gain_ratio_6 (numeric)],
8: [8 - gain_ratio_7 (numeric)],
9: [9 - gain_ratio_8 (numeric... | {'MajorityClassSize': 177.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 163.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 340.0,
'NumberOfInstancesWithMissingValues': 228.0,
'NumberOfMissingValues': 834.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 3.... | schizo | [
"ID",
"target",
"gain_ratio_1",
"gain_ratio_2",
"gain_ratio_3",
"gain_ratio_4",
"gain_ratio_5",
"gain_ratio_6",
"gain_ratio_7",
"gain_ratio_8",
"gain_ratio_9",
"gain_ratio_10",
"gain_ratio_11",
"sex"
] | [
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 1,957 |
4,257 | predictive_accuracy | accuracy_score | analcatdata_creditscore | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Age (numeric)],
1: [1 - Income.per.dependent (numeric)],
2: [2 - Monthly.credit.card.exp (numeric)],
3: [3 - Own.home (nominal)],
4: [4 - Self.employed (nominal)],
5: [5 - Derogatory.reports (nominal)],
6: [6 - Application.accepted (nominal)]} | {'MajorityClassSize': 73.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 27.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 4.0,
'cos... | analcatdata_creditscore | [
"Age",
"Income.per.dependent",
"Monthly.credit.card.exp",
"Own.home",
"Self.employed",
"Derogatory.reports"
] | [
false,
false,
false,
true,
true,
true
] | 1,958 |
4,259 | predictive_accuracy | accuracy_score | backache | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data file:
This data from "Problem-Solving" on "backache in pregnancy"
is in somewhat different
format from that listed in the book. Each integer is preceded by a space.
This makes it easier to read. Each line is split in two separated by an
amper... | {0: [0 - id (numeric)],
1: [1 - col_2 (nominal)],
2: [2 - col_3 (nominal)],
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 (nominal)],
9: [9 - col_10 (nominal)],
10: [10 - col_11 (nominal)],
11: [11 - col_12 (nom... | {'MajorityClassSize': 155.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 25.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 32.0,
'NumberOfInstances': 180.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 27.0,
... | backache | [
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9",
"col_10",
"col_11",
"col_12",
"col_13",
"col_14",
"col_15",
"col_16",
"col_17",
"col_18",
"col_19",
"col_20",
"col_21",
"col_22",
"col_23",
"col_24",
"col_25",
"col_26",
"col_27",
"col_28",
... | [
true,
true,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,959 |
4,251 | predictive_accuracy | accuracy_score | analcatdata_halloffame | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Player (nominal)],
1: [1 - Number_seasons (numeric)],
2: [2 - Games_played (numeric)],
3: [3 - At_bats (numeric)],
4: [4 - Runs (numeric)],
5: [5 - Hits (numeric)],
6: [6 - Doubles (numeric)],
7: [7 - Triples (numeric)],
8: [8 - Home_runs (numeric)],
9: [9 - RBIs (numeric)],
10: [10 - Walks (numeric)... | {'MajorityClassSize': 1215.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 57.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 1340.0,
'NumberOfInstancesWithMissingValues': 20.0,
'NumberOfMissingValues': 20.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 2.0... | analcatdata_halloffame | [
"Number_seasons",
"Games_played",
"At_bats",
"Runs",
"Hits",
"Doubles",
"Triples",
"Home_runs",
"RBIs",
"Walks",
"Strikeouts",
"Batting_average",
"On_base_pct",
"Slugging_pct",
"Fielding_ave",
"Position"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 1,960 |
4,258 | predictive_accuracy | accuracy_score | analcatdata_challenger | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Date (nominal)],
1: [1 - Temperature (numeric)],
2: [2 - Pressure (nominal)],
3: [3 - Damaged (nominal)],
4: [4 - O-rings (nominal)],
5: [5 - Nozzle_joint (nominal)]} | {'MajorityClassSize': 16.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 23.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 4.0,
'cost_... | analcatdata_challenger | [
"Temperature",
"Pressure",
"O-rings",
"Nozzle_joint"
] | [
false,
true,
true,
true
] | 1,961 |
4,260 | predictive_accuracy | accuracy_score | prnn_synth | **Author**: B.D. Ripley
**Source**: Unknown - Date unknown
**Please cite**:
Dataset from `Pattern Recognition and Neural Networks' by B.D. Ripley. Cambridge University Press (1996) ISBN 0-521-46086-7. The background to the datasets is described in section 1.4; this file relates the computer-readable files to ... | {0: [0 - xs (numeric)], 1: [1 - ys (numeric)], 2: [2 - yc (nominal)]} | {'MajorityClassSize': 125.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 125.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 250.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | prnn_synth | [
"xs",
"ys"
] | [
false,
false
] | 1,962 |
4,215 | predictive_accuracy | accuracy_score | JapaneseVowels | **Author**: Mineichi Kudo, Jun Toyama, Masaru Shimbo
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels)
**Please cite**:
**Japanese vowels**
This dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers.
The data was collected for examining our... | {0: [0 - speaker (nominal)],
1: [1 - utterance (numeric)],
2: [2 - frame (numeric)],
3: [3 - coefficient1 (numeric)],
4: [4 - coefficient2 (numeric)],
5: [5 - coefficient3 (numeric)],
6: [6 - coefficient4 (numeric)],
7: [7 - coefficient5 (numeric)],
8: [8 - coefficient6 (numeric)],
9: [9 - coefficient7 (numeri... | {'MajorityClassSize': 1614.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 782.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 9961.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 1.0,... | JapaneseVowels | [
"utterance",
"frame",
"coefficient1",
"coefficient2",
"coefficient3",
"coefficient4",
"coefficient5",
"coefficient6",
"coefficient7",
"coefficient8",
"coefficient9",
"coefficient10",
"coefficient11",
"coefficient12"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,963 |
4,269 | predictive_accuracy | accuracy_score | analcatdata_germangss | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Political_system (nominal)],
1: [1 - Age (nominal)],
2: [2 - Time_of_survey (nominal)],
3: [3 - Schooling (nominal)],
4: [4 - Region (nominal)],
5: [5 - Count (numeric)]} | {'MajorityClassSize': 100.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 100.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 5.0,
'c... | analcatdata_germangss | [
"Age",
"Time_of_survey",
"Schooling",
"Region",
"Count"
] | [
true,
true,
true,
true,
false
] | 1,965 |
4,267 | predictive_accuracy | accuracy_score | lupus | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
87 persons with lupus nephritis. Followed up 15+ years. 35 deaths. Var =
duration of disease. Over 40 baseline variables avaiable from authors.
Description :
For description of this data set arising from 87 persons
with lupus nephritis followed fo... | {0: [0 - TIME (numeric)],
1: [1 - STATUS (nominal)],
2: [2 - DURATION (numeric)],
3: [3 - LOG(1+DURATION) (numeric)]} | {'MajorityClassSize': 52.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 35.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 87.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | lupus | [
"TIME",
"DURATION",
"LOG(1+DURATION)"
] | [
false,
false,
false
] | 1,966 |
4,266 | predictive_accuracy | accuracy_score | profb | **Author**: Hal Stern, Robin Lock
**Source**: [StatLib](http://lib.stat.cmu.edu/datasets/profb)
**Please cite**:
PRO FOOTBALL SCORES (raw data appears after the description below)
How well do the oddsmakers of Las Vegas predict the outcome of
professional football games? Is there really a home field advanta... | {0: [0 - Home/Away (nominal)],
1: [1 - Favorite_Points (numeric)],
2: [2 - Underdog_Points (numeric)],
3: [3 - Pointspread (numeric)],
4: [4 - Favorite_Name (nominal)],
5: [5 - Underdog_name (nominal)],
6: [6 - Year (numeric)],
7: [7 - Week (numeric)],
8: [8 - Weekday (nominal)],
9: [9 - Overtime (nominal)]} | {'MajorityClassSize': 448.0,
'MaxNominalAttDistinctValues': 28.0,
'MinorityClassSize': 224.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 672.0,
'NumberOfInstancesWithMissingValues': 666.0,
'NumberOfMissingValues': 1200.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 5... | profb | [
"Favorite_Points",
"Underdog_Points",
"Pointspread",
"Favorite_Name",
"Underdog_name",
"Year",
"Week",
"Weekday",
"Overtime"
] | [
false,
false,
false,
true,
true,
false,
false,
true,
true
] | 1,967 |
211,951 | 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': 3707.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 75.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1101.0,
'NumberOfInstances': 3782.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1079.0,
'NumberOfSymbolicFeatures': 2... | slashdot | [
"Entertainment",
"Interviews",
"Main",
"Developers",
"Apache",
"News",
"Search",
"Mobile",
"Science",
"IT",
"BSD",
"Idle",
"Games",
"YourRightsOnline",
"AskSlashdot",
"Apple",
"BookReviews",
"Hardware",
"Meta",
"Linux",
"Politics",
"Technology",
"X0",
"X000",
"X1",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false... | 1,968 |
4,264 | predictive_accuracy | accuracy_score | confidence | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
CODING:
ITEM 1 = BUSINESS CONDIDIONS 6 MONTHS FROM NOW (CONFERENCE BOARD)
ITEM 2 = JOBS 6 MONTHS FROM NOW (CONFERENCE BOARD)
ITEM 3 = FAMILY INCOME 6 MONTHS FROM NOW (CONFERENCE BOARD)
ITEM 4 = BUSINESS CONDITIONS A YEAR FROM NOW (MICHIGAN)
IT... | {0: [0 - ITEM (nominal)],
1: [1 - P (numeric)],
2: [2 - N (numeric)],
3: [3 - O (numeric)]} | {'MajorityClassSize': 12.0,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': 12.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 72.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | confidence | [
"P",
"N",
"O"
] | [
false,
false,
false
] | 1,969 |
4,270 | predictive_accuracy | accuracy_score | analcatdata_bankruptcy | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Company (nominal)],
1: [1 - WC/TA (numeric)],
2: [2 - RE/TA (numeric)],
3: [3 - EBIT/TA (numeric)],
4: [4 - S/TA (numeric)],
5: [5 - BVE/BVL (numeric)],
6: [6 - Bankrupt (nominal)]} | {'MajorityClassSize': 25.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 25.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | analcatdata_bankruptcy | [
"WC/TA",
"RE/TA",
"EBIT/TA",
"S/TA",
"BVE/BVL"
] | [
false,
false,
false,
false,
false
] | 1,970 |
4,265 | predictive_accuracy | accuracy_score | analcatdata_dmft | **Author**: Unknown
**Source**: [Jeffrey S. Simonoff](http://people.stern.nyu.edu/jsimonof/AnalCatData/Data/) - 2003
**Please cite**: Jeffrey S. Simonoff, Analyzing Categorical Data, Springer-Verlag, 2003
One of the datasets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff. It contains data... | {0: [0 - DMFT.Begin (nominal)],
1: [1 - DMFT.End (nominal)],
2: [2 - Gender (nominal)],
3: [3 - Ethnic (nominal)],
4: [4 - Prevention (nominal)]} | {'MajorityClassSize': 155.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 123.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 797.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 5.0,
'c... | analcatdata_dmft | [
"DMFT.Begin",
"DMFT.End",
"Gender",
"Ethnic"
] | [
true,
true,
true,
true
] | 1,971 |
4,274 | predictive_accuracy | accuracy_score | prnn_viruses | **Author**: B.D. Ripley
**Source**: StatLib - Date unknown
**Please cite**:
Dataset from `Pattern Recognition and Neural Networks' by B.D. Ripley. Cambridge University Press (1996) ISBN 0-521-46086-7
The background to the datasets is described in section 1.4; this file relates the computer-readable files to ... | {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 (nominal)],
8: [8 - col_9 (numeric)],
9: [9 - col_10 (nominal)],
10: [10 - col_11 (nominal)],
11: [11 - col_12 (... | {'MajorityClassSize': 39.0,
'MaxNominalAttDistinctValues': 10.0,
'MinorityClassSize': 3.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 61.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 9.0,
'co... | prnn_viruses | [
"col_1",
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9",
"col_10",
"col_11",
"col_12",
"col_13",
"col_14",
"col_15",
"col_16",
"col_17",
"col_18"
] | [
false,
false,
false,
false,
false,
false,
false,
true,
false,
true,
true,
true,
true,
true,
true,
false,
false,
true
] | 1,972 |
4,268 | predictive_accuracy | accuracy_score | analcatdata_marketing | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - X1a (nominal)],
1: [1 - X1b (nominal)],
2: [2 - X1c (nominal)],
3: [3 - X1d (nominal)],
4: [4 - X1e (nominal)],
5: [5 - X1f (nominal)],
6: [6 - X1g (nominal)],
7: [7 - X1h (nominal)],
8: [8 - X1i (nominal)],
9: [9 - X1j (nominal)],
10: [10 - X1k (nominal)],
11: [11 - X1l (nominal)],
12: [12 - X1m (... | {'MajorityClassSize': 203.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 2.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 364.0,
'NumberOfInstancesWithMissingValues': 53.0,
'NumberOfMissingValues': 101.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 33.0,
... | analcatdata_marketing | [
"X1b",
"X1c",
"X1d",
"X1e",
"X1f",
"X1g",
"X1h",
"X1i",
"X1j",
"X1k",
"X1l",
"X1m",
"X1n",
"X1o",
"X2a",
"X2b",
"X2c",
"X2d",
"X2e",
"X2f",
"X2g",
"X2h",
"X2i",
"X2j",
"X2k",
"X2l",
"X2m",
"X3a",
"X3b",
"X3c",
"X5",
"X9"
] | [
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
] | 1,973 |
4,191 | predictive_accuracy | accuracy_score | spectrometer | **Author**:
**Source**: Unknown - 1988
**Please cite**:
1. Title: Part of the IRAS Low Resolution Spectrometer Database
2. Sources:
(a) Originator: Infra-Red Astronomy Satellite Project Database
(b) Donor: John Stutz <STUTZ@pluto.arc.nasa.gov>
(c) Date: March 1988 (approximately)
3. Past Usage: unknown
... | {0: [0 - LRS-name (nominal)],
1: [1 - LRS-class (nominal)],
2: [2 - ID-type (nominal)],
3: [3 - Right-Ascension (numeric)],
4: [4 - Declination (numeric)],
5: [5 - Scale_Factor (numeric)],
6: [6 - Blue_base_1 (numeric)],
7: [7 - Blue_base_2 (numeric)],
8: [8 - Red_base_1 (numeric)],
9: [9 - Red_base_2 (numeric... | {'MajorityClassSize': 55.0,
'MaxNominalAttDistinctValues': 531.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 48.0,
'NumberOfFeatures': 102.0,
'NumberOfInstances': 531.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 2.0,... | spectrometer | [
"ID-type",
"Right-Ascension",
"Declination",
"Scale_Factor",
"Blue_base_1",
"Blue_base_2",
"Red_base_1",
"Red_base_2",
"blue-band-flux_1",
"blue-band-flux_2",
"blue-band-flux_3",
"blue-band-flux_4",
"blue-band-flux_5",
"blue-band-flux_6",
"blue-band-flux_7",
"blue-band-flux_8",
"blue... | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
fa... | 1,974 |
4,275 | predictive_accuracy | accuracy_score | biomed | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
February 23, 1982
The 1982 annual meetings of the American Statistical Association (ASA)
will be held August 16-19, 1982 in Cincinnati. At that meeting, the ASA
Committee on Statistical Graphics plans to sponsor an "Exposition of
Statistical Gra... | {0: [0 - Observation_number (nominal)],
1: [1 - Hospital_identification_number_for_blood_sample (numeric)],
2: [2 - Age_of_patient (numeric)],
3: [3 - Date_that_blood_sample_was_taken (numeric)],
4: [4 - ml (numeric)],
5: [5 - m2 (numeric)],
6: [6 - m3 (numeric)],
7: [7 - m4 (numeric)],
8: [8 - class (nominal)]... | {'MajorityClassSize': 134.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 75.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 209.0,
'NumberOfInstancesWithMissingValues': 15.0,
'NumberOfMissingValues': 15.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 2.0,
'... | biomed | [
"Observation_number",
"Hospital_identification_number_for_blood_sample",
"Age_of_patient",
"Date_that_blood_sample_was_taken",
"ml",
"m2",
"m3",
"m4"
] | [
true,
false,
false,
false,
false,
false,
false,
false
] | 1,975 |
4,273 | predictive_accuracy | accuracy_score | analcatdata_cyyoung9302 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - Year (nominal)],
1: [1 - Pitcher (nominal)],
2: [2 - League (nominal)],
3: [3 - Type (nominal)],
4: [4 - Wins (numeric)],
5: [5 - Win_pct (numeric)],
6: [6 - Saves (numeric)],
7: [7 - ERA (numeric)],
8: [8 - Strikeouts (numeric)],
9: [9 - Innings_pitched (numeric)],
10: [10 - Cy_Young (nominal)]} | {'MajorityClassSize': 73.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 19.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 92.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 4.0,
'cos... | analcatdata_cyyoung9302 | [
"Year",
"League",
"Type",
"Wins",
"Win_pct",
"Saves",
"ERA",
"Strikeouts",
"Innings_pitched"
] | [
true,
true,
true,
false,
false,
false,
false,
false,
false
] | 1,976 |
4,271 | predictive_accuracy | accuracy_score | fl2000 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
County data from the 2000 Presidential Election in Florida.
Compiled by Brett Presnell
Department of Statistics, University of Florida
These data are derived from three sources, described below. As far
as I am aware, you are free to use these d... | {0: [0 - county (nominal)],
1: [1 - technology (nominal)],
2: [2 - columns (nominal)],
3: [3 - under (numeric)],
4: [4 - over (numeric)],
5: [5 - Bush (numeric)],
6: [6 - Gore (numeric)],
7: [7 - Browne (numeric)],
8: [8 - Nader (numeric)],
9: [9 - Harris (numeric)],
10: [10 - Hagelin (numeric)],
11: [11 - B... | {'MajorityClassSize': 41.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 67.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | fl2000 | [
"columns",
"under",
"over",
"Bush",
"Gore",
"Browne",
"Nader",
"Harris",
"Hagelin",
"Buchanan",
"McReynolds",
"Phillips",
"Moorehead",
"Chote",
"McCarthy"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,977 |
4,283 | predictive_accuracy | accuracy_score | diggle_table_a2 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
DATA-SETS FROM DIGGLE, P.J. (1990). TIME SERIES : A BIOSTATISTICAL
INTRODUCTION. Oxford University Press.
Table: Table A2 Wool prices
Information about the dataset
CLASSTYPE: numeric
CLASSINDEX: none specific | {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 - col_9 (numeric)]} | {'MajorityClassSize': 41.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 18.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 310.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | diggle_table_a2 | [
"col_2",
"col_3",
"col_4",
"col_5",
"col_6",
"col_7",
"col_8",
"col_9"
] | [
false,
false,
false,
false,
false,
false,
false,
false
] | 1,978 |
4,279 | predictive_accuracy | accuracy_score | rmftsa_sleepdata | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data Sets for 'Regression Models for Time Series Analysis' by
B. Kedem and K. Fokianos, Wiley 2002. Submitted by Kostas
Fokianos (fokianos@ucy.ac.cy) [8/Nov/02] (176k)
Note: - attribute names were generated manually
- information about data taken... | {0: [0 - heart_rate (numeric)],
1: [1 - sleep_state (nominal)],
2: [2 - temperature (numeric)]} | {'MajorityClassSize': 404.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 94.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1024.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | rmftsa_sleepdata | [
"heart_rate",
"temperature"
] | [
false,
false
] | 1,979 |
4,282 | predictive_accuracy | accuracy_score | visualizing_livestock | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
This S dump contains 22 data sets from the
book Visualizing Data published by
Hobart Press (books@hobart.com).
The dump was created by data.dump()
and can be read back into S by data.restore().
The name of each S data set is the name of
the data s... | {0: [0 - livestocktype (nominal)],
1: [1 - country (nominal)],
2: [2 - count (numeric)]} | {'MajorityClassSize': 26.0,
'MaxNominalAttDistinctValues': 26.0,
'MinorityClassSize': 26.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 130.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 2.0,
'co... | visualizing_livestock | [
"country",
"count"
] | [
true,
false
] | 1,980 |
4,280 | predictive_accuracy | accuracy_score | sleuth_ex2016 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Contains 110 data sets from the book 'The Statistical Sleuth'
by Fred Ramsey and Dan Schafer; Duxbury Press, 1997.
(schafer@stat.orst.edu) [14/Oct/97] (172k)
Note: description taken from this web site:
http://lib.stat.cmu.edu/datasets/
File: ../... | {0: [0 - sv (nominal)],
1: [1 - ag (nominal)],
2: [2 - tl (numeric)],
3: [3 - ae (numeric)],
4: [4 - wt (numeric)],
5: [5 - bh (numeric)],
6: [6 - hl (numeric)],
7: [7 - fl (numeric)],
8: [8 - tt (numeric)],
9: [9 - sk (numeric)],
10: [10 - kl (numeric)]} | {'MajorityClassSize': 51.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 87.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | sleuth_ex2016 | [
"ag",
"tl",
"ae",
"wt",
"bh",
"hl",
"fl",
"tt",
"sk",
"kl"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,981 |
212,079 | 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': 3707.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 75.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1101.0,
'NumberOfInstances': 3782.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1079.0,
'NumberOfSymbolicFeatures': 2... | slashdot | [
"Entertainment",
"Interviews",
"Main",
"Developers",
"Apache",
"News",
"Search",
"Mobile",
"Science",
"IT",
"BSD",
"Idle",
"Games",
"YourRightsOnline",
"AskSlashdot",
"Apple",
"BookReviews",
"Hardware",
"Meta",
"Linux",
"Politics",
"Technology",
"X0",
"X000",
"X1",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false... | 1,982 |
4,281 | predictive_accuracy | accuracy_score | sleuth_ex2015 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Contains 110 data sets from the book 'The Statistical Sleuth'
by Fred Ramsey and Dan Schafer; Duxbury Press, 1997.
(schafer@stat.orst.edu) [14/Oct/97] (172k)
Note: description taken from this web site:
http://lib.stat.cmu.edu/datasets/
File: ../... | {0: [0 - owl (nominal)],
1: [1 - pctring1 (numeric)],
2: [2 - pctring2 (numeric)],
3: [3 - pctring3 (numeric)],
4: [4 - pctring4 (numeric)],
5: [5 - pctring5 (numeric)],
6: [6 - pctring6 (numeric)],
7: [7 - pctring7 (numeric)]} | {'MajorityClassSize': 30.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 30.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 60.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | sleuth_ex2015 | [
"pctring1",
"pctring2",
"pctring3",
"pctring4",
"pctring5",
"pctring6",
"pctring7"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,983 |
4,285 | predictive_accuracy | accuracy_score | fruitfly | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - PARTNERS (nominal)],
1: [1 - TYPE (nominal)],
2: [2 - THORAX (numeric)],
3: [3 - SLEEP (numeric)],
4: [4 - binaryClass (nominal)]} | {'MajorityClassSize': 76.0,
'MaxNominalAttDistinctValues': 3.0,
'MinorityClassSize': 49.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 125.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cos... | fruitfly | [
"PARTNERS",
"TYPE",
"THORAX",
"SLEEP"
] | [
true,
true,
false,
false
] | 1,984 |
4,287 | predictive_accuracy | accuracy_score | fri_c3_100_50 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz7 (numeric)],
7: [7 - oz8 (numeric)],
8: [8 - oz9 (numeric)],
9: [9 - oz10 (numeric)],
10: [10 - oz11 (numeric)],
11: [11 - oz12 (numeric)],
12: [12 - oz... | {'MajorityClassSize': 62.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 38.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | fri_c3_100_50 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz7",
"oz8",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz14",
"oz15",
"oz16",
"oz17",
"oz18",
"oz19",
"oz20",
"oz21",
"oz22",
"oz23",
"oz24",
"oz25",
"oz26",
"oz27",
"oz28",
"oz29",
"oz30",
"oz31",
"oz32",
"oz33... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,985 |
4,254 | predictive_accuracy | accuracy_score | analcatdata_authorship | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
analcatdata A collection of data sets used in the book "Analyzing Categorical Data,"
by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission
consists of a zip file containing two versions of each of 84 data sets,
plus this READM... | {0: [0 - a (numeric)],
1: [1 - all (numeric)],
2: [2 - also (numeric)],
3: [3 - an (numeric)],
4: [4 - and (numeric)],
5: [5 - any (numeric)],
6: [6 - are (numeric)],
7: [7 - as (numeric)],
8: [8 - at (numeric)],
9: [9 - be (numeric)],
10: [10 - been (numeric)],
11: [11 - but (numeric)],
12: [12 - by (numer... | {'MajorityClassSize': 317.0,
'MaxNominalAttDistinctValues': 4.0,
'MinorityClassSize': 55.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 71.0,
'NumberOfInstances': 841.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 70.0,
'NumberOfSymbolicFeatures': 1.0,
'... | analcatdata_authorship | [
"a",
"all",
"also",
"an",
"and",
"any",
"are",
"as",
"at",
"be",
"been",
"but",
"by",
"can",
"do",
"down",
"even",
"every",
"for",
"from",
"had",
"has",
"have",
"her",
"his",
"if",
"in",
"into",
"is",
"it",
"its",
"may",
"more",
"must",
"my",
"no... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,987 |
211,809 | 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': 3707.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 75.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1101.0,
'NumberOfInstances': 3782.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1079.0,
'NumberOfSymbolicFeatures': 2... | slashdot | [
"Entertainment",
"Interviews",
"Main",
"Developers",
"Apache",
"News",
"Search",
"Mobile",
"Science",
"IT",
"BSD",
"Idle",
"Games",
"YourRightsOnline",
"AskSlashdot",
"Apple",
"BookReviews",
"Hardware",
"Meta",
"Linux",
"Politics",
"Technology",
"X0",
"X000",
"X1",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false... | 1,988 |
4,276 | predictive_accuracy | accuracy_score | colleges_aaup | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The AAUP dataset for the ASA Statistical Graphics Section's 1995
Data Analysis Exposition contains information on faculty salaries
for 1161 American colleges and universities. The data may be
obtained in either of two formats.
AAUP.DATA contains... | {0: [0 - FICE (numeric)],
1: [1 - College_name (nominal)],
2: [2 - State (nominal)],
3: [3 - Type (nominal)],
4: [4 - Average_salary-full_professors (numeric)],
5: [5 - Average_salary-associate_professors (numeric)],
6: [6 - Average_salary-assistant_professors (numeric)],
7: [7 - Average_salary-all_ranks (numeri... | {'MajorityClassSize': 617.0,
'MaxNominalAttDistinctValues': 52.0,
'MinorityClassSize': 1.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 1161.0,
'NumberOfInstancesWithMissingValues': 87.0,
'NumberOfMissingValues': 256.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 2.0... | colleges_aaup | [
"State",
"Average_salary-full_professors",
"Average_salary-associate_professors",
"Average_salary-assistant_professors",
"Average_salary-all_ranks",
"Average_compensation-full_professors",
"Average_compensation-associate_professors",
"Average_compensation-assistant_professors",
"Average_compensation... | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,990 |
4,292 | predictive_accuracy | accuracy_score | pwLinear | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others... | {0: [0 - a1 (numeric)],
1: [1 - a2 (numeric)],
2: [2 - a3 (numeric)],
3: [3 - a4 (numeric)],
4: [4 - a5 (numeric)],
5: [5 - a6 (numeric)],
6: [6 - a7 (numeric)],
7: [7 - a8 (numeric)],
8: [8 - a9 (numeric)],
9: [9 - a10 (numeric)],
10: [10 - binaryClass (nominal)]} | {'MajorityClassSize': 103.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 97.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 200.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 1.0,
'... | pwLinear | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"a7",
"a8",
"a9",
"a10"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,991 |
4,295 | predictive_accuracy | accuracy_score | analcatdata_vineyard | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class 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 (nominal)],
1: [1 - Row (numeric)],
2: [2 - Group (numeric)],
3: [3 - binaryClass (nominal)]} | {'MajorityClassSize': 260.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 208.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 468.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 2.0,
'c... | analcatdata_vineyard | [
"Year",
"Row",
"Group"
] | [
true,
false,
false
] | 1,992 |
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