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 |
|---|---|---|---|---|---|---|---|---|---|---|
363,545 | predictive_accuracy | accuracy_score | HCV_data | Abstract of the paper (https://jlpm.amegroups.org/article/view/4401):
"
Background: Diagnostic pathways are based on expert rules ("if...then...else"), which can be visualized as decision trees. Machine learning algorithms may be used to validate existing or to suggest potential new decision trees.
Methods: We present... | {0: [0 - Age (numeric)],
1: [1 - Sex (string)],
2: [2 - ALB (numeric)],
3: [3 - ALP (numeric)],
4: [4 - AST (numeric)],
5: [5 - BIL (numeric)],
6: [6 - CHE (numeric)],
7: [7 - CHOL (numeric)],
8: [8 - CREA (numeric)],
9: [9 - CGT (numeric)],
10: [10 - PROT (numeric)],
11: [11 - ALT (numeric)],
12: [12 - Cat... | {'MajorityClassSize': 533.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 7.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 615.0,
'NumberOfInstancesWithMissingValues': 26.0,
'NumberOfMissingValues': 31.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0,
... | HCV_data | [
"Age",
"Sex",
"ALB",
"ALP",
"AST",
"BIL",
"CHE",
"CHOL",
"CREA",
"CGT",
"PROT",
"ALT"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,569 |
361,880 | predictive_accuracy | accuracy_score | timing-attack-dataset-25-micro-seconds-delay-2022-09-21 | Bleichenbacher Timing Attack: 25 micro seconds dataset created on 2022-09-21
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination ... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 955.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 866.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9994.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-25-micro-seconds-delay-2022-09-21 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,570 |
362,994 | predictive_accuracy | accuracy_score | guillermo_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset guillermo (41159) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - V84 (numeric)],
1: [1 - V117 (numeric)],
2: [2 - V147 (numeric)],
3: [3 - V170 (numeric)],
4: [4 - V233 (numeric)],
5: [5 - V263 (numeric)],
6: [6 - V267 (numeric)],
7: [7 - V362 (numeric)],
8: [8 - V392 (numeric)],
9: [9 - V494 (numeric)],
10: [10 - V497 (numeric)],
11: [11 - V524 (numeric)],
12: ... | {'MajorityClassSize': 1200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 800.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | guillermo_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V84",
"V117",
"V147",
"V170",
"V233",
"V263",
"V267",
"V362",
"V392",
"V494",
"V497",
"V524",
"V526",
"V567",
"V606",
"V636",
"V684",
"V862",
"V920",
"V1049",
"V1083",
"V1112",
"V1115",
"V1150",
"V1178",
"V1189",
"V1246",
"V1248",
"V1282",
"V1312",
"V1373... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,572 |
363,145 | predictive_accuracy | accuracy_score | LoginDataForCE | LoginDataForCE | {0: [0 - TimeStamp (string)],
1: [1 - Message (string)],
2: [2 - SourceIP (string)],
3: [3 - EventID (numeric)],
4: [4 - TargetUserName (string)],
5: [5 - Task (string)],
6: [6 - id (numeric)],
7: [7 - Label (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 1441.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | LoginDataForCE | [
"TimeStamp",
"Message",
"SourceIP",
"EventID",
"TargetUserName",
"id",
"Label"
] | [
false,
false,
false,
false,
false,
false,
false
] | 3,573 |
363,549 | predictive_accuracy | accuracy_score | Estimation_of_Obesity_Levels | This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records, the records are labeled with the class variable NObesity (Obesity Level), that allows clas... | {0: [0 - Gender (string)],
1: [1 - Age (numeric)],
2: [2 - Height (numeric)],
3: [3 - Weight (numeric)],
4: [4 - family_history_with_overweight (string)],
5: [5 - FAVC (string)],
6: [6 - FCVC (numeric)],
7: [7 - NCP (numeric)],
8: [8 - CAEC (string)],
9: [9 - SMOKE (string)],
10: [10 - CH2O (numeric)],
11: [... | {'MajorityClassSize': 351.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 272.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 2111.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
... | Estimation_of_Obesity_Levels | [
"Gender",
"Age",
"Height",
"Weight",
"family_history_with_overweight",
"FAVC",
"FCVC",
"NCP",
"CAEC",
"SMOKE",
"CH2O",
"SCC",
"FAF",
"TUE",
"CALC",
"MTRANS"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,574 |
363,017 | predictive_accuracy | accuracy_score | Click_prediction_small_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Click_prediction_small (42733) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
ncl... | {0: [0 - impression (numeric)],
1: [1 - url_hash (numeric)],
2: [2 - ad_id (nominal)],
3: [3 - advertiser_id (nominal)],
4: [4 - depth (numeric)],
5: [5 - position (numeric)],
6: [6 - query_id (numeric)],
7: [7 - keyword_id (nominal)],
8: [8 - title_id (nominal)],
9: [9 - description_id (nominal)],
10: [10 - ... | {'MajorityClassSize': 1663.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 337.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 7.0,
... | Click_prediction_small_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"impression",
"url_hash",
"ad_id",
"advertiser_id",
"depth",
"position",
"query_id",
"keyword_id",
"title_id",
"description_id",
"user_id"
] | [
false,
false,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 3,575 |
363,546 | predictive_accuracy | accuracy_score | SOCC | SOCC
SFU Opinion and Comments Corpus
The SFU Opinion and Comments Corpus (SOCC) is a corpus for the analysis of online news comments. Our corpus contains comments and the articles from which the comments originated. The articles are all opinion articles, not hard news articles. The corpus is larger than any other curr... | {0: [0 - article_id (numeric)],
1: [1 - comment_counter (string)],
2: [2 - title (string)],
3: [3 - globe_url (string)],
4: [4 - url (string)],
5: [5 - comment_text (string)],
6: [6 - is_constructive (string)],
7: [7 - is_constructive_confidence (numeric)],
8: [8 - toxicity_level (nominal)],
9: [9 - toxicity_l... | {'MajorityClassSize': 829.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 7.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 1043.0,
'NumberOfInstancesWithMissingValues': 983.0,
'NumberOfMissingValues': 2641.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 1.0... | SOCC | [
"article_id",
"comment_counter",
"title",
"globe_url",
"url",
"comment_text",
"is_constructive",
"is_constructive_confidence",
"toxicity_level_confidence",
"did_you_read_the_article",
"did_you_read_the_article_confidence",
"annotator_comments",
"expert_is_constructive",
"expert_toxicity_le... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,576 |
363,163 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_DOG_Mini | Dogs dataset with different breeds of dogs | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_DOG_Mini | [
"FILE_NAME"
] | [
false
] | 3,577 |
363,014 | predictive_accuracy | accuracy_score | Click_prediction_small_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Click_prediction_small (42733) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
ncl... | {0: [0 - impression (numeric)],
1: [1 - url_hash (numeric)],
2: [2 - ad_id (nominal)],
3: [3 - advertiser_id (nominal)],
4: [4 - depth (numeric)],
5: [5 - position (numeric)],
6: [6 - query_id (numeric)],
7: [7 - keyword_id (nominal)],
8: [8 - title_id (nominal)],
9: [9 - description_id (nominal)],
10: [10 - ... | {'MajorityClassSize': 1663.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 337.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 7.0,
... | Click_prediction_small_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"impression",
"url_hash",
"ad_id",
"advertiser_id",
"depth",
"position",
"query_id",
"keyword_id",
"title_id",
"description_id",
"user_id"
] | [
false,
false,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 3,578 |
363,553 | predictive_accuracy | accuracy_score | Predicting_Risk_Factors_of_Chronic_Kidney_Disease | Was there any data preprocessing performed?
This dataset is not pre-processed, if you want to apply a Machine learning Algorithm at first you have to need to pre-process the data
Additional Information
This dataset is real Bangladeshi patient data. The dataset is collected from Enam Medical College, Savar, Dhaka, Ba... | {0: [0 - bp (Diastolic) (numeric)],
1: [1 - bp limit (numeric)],
2: [2 - sg (string)],
3: [3 - al (string)],
4: [4 - rbc (numeric)],
5: [5 - su (string)],
6: [6 - pc (numeric)],
7: [7 - pcc (numeric)],
8: [8 - ba (numeric)],
9: [9 - bgr (string)],
10: [10 - bu (string)],
11: [11 - sod (string)],
12: [12 - s... | {'MajorityClassSize': 128.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 72.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 29.0,
'NumberOfInstances': 200.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 1.0,
'... | Predicting_Risk_Factors_of_Chronic_Kidney_Disease | [
"bp (Diastolic)",
"bp limit",
"sg",
"al",
"rbc",
"su",
"pc",
"pcc",
"ba",
"bgr",
"bu",
"sod",
"sc",
"pot",
"hemo",
"pcv",
"rbcc",
"wbcc",
"htn",
"dm",
"cad",
"appet",
"pe",
"ane",
"grf",
"stage",
"affected",
"age"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,579 |
363,013 | predictive_accuracy | accuracy_score | Click_prediction_small_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Click_prediction_small (42733) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
ncl... | {0: [0 - impression (numeric)],
1: [1 - url_hash (numeric)],
2: [2 - ad_id (nominal)],
3: [3 - advertiser_id (nominal)],
4: [4 - depth (numeric)],
5: [5 - position (numeric)],
6: [6 - query_id (numeric)],
7: [7 - keyword_id (nominal)],
8: [8 - title_id (nominal)],
9: [9 - description_id (nominal)],
10: [10 - ... | {'MajorityClassSize': 1663.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 337.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 7.0,
... | Click_prediction_small_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"impression",
"url_hash",
"ad_id",
"advertiser_id",
"depth",
"position",
"query_id",
"keyword_id",
"title_id",
"description_id",
"user_id"
] | [
false,
false,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 3,580 |
363,552 | predictive_accuracy | accuracy_score | Cirrhosis_Patient_Survival_Prediction | For what purpose was the dataset created?
Cirrhosis results from prolonged liver damage, leading to extensive scarring, often due to conditions like hepatitis or chronic alcohol consumption. The data provided is sourced from a Mayo Clinic study on primary biliary cirrhosis (PBC) of the liver carried out from 1974 to 1... | {0: [0 - Drug (string)],
1: [1 - Age (numeric)],
2: [2 - Sex (string)],
3: [3 - Ascites (string)],
4: [4 - Hepatomegaly (string)],
5: [5 - Spiders (string)],
6: [6 - Edema (string)],
7: [7 - Bilirubin (numeric)],
8: [8 - Cholesterol (string)],
9: [9 - Albumin (numeric)],
10: [10 - Copper (string)],
11: [11 -... | {'MajorityClassSize': 232.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 25.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 18.0,
'NumberOfInstances': 418.0,
'NumberOfInstancesWithMissingValues': 106.0,
'NumberOfMissingValues': 965.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0,... | Cirrhosis_Patient_Survival_Prediction | [
"Drug",
"Age",
"Sex",
"Ascites",
"Hepatomegaly",
"Spiders",
"Edema",
"Bilirubin",
"Cholesterol",
"Albumin",
"Copper",
"Alk_Phos",
"SGOT",
"Tryglicerides",
"Platelets",
"Prothrombin",
"Stage"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,581 |
363,171 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_MED_LF_Mini | Healthy Medicinal Leaf | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_MED_LF_Mini | [
"FILE_NAME"
] | [
false
] | 3,582 |
363,001 | predictive_accuracy | accuracy_score | riccardo_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset riccardo (41161) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int... | {0: [0 - V7 (numeric)],
1: [1 - V21 (numeric)],
2: [2 - V130 (numeric)],
3: [3 - V138 (numeric)],
4: [4 - V166 (numeric)],
5: [5 - V183 (numeric)],
6: [6 - V327 (numeric)],
7: [7 - V360 (numeric)],
8: [8 - V388 (numeric)],
9: [9 - V396 (numeric)],
10: [10 - V440 (numeric)],
11: [11 - V480 (numeric)],
12: [1... | {'MajorityClassSize': 1500.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 500.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | riccardo_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V7",
"V21",
"V130",
"V138",
"V166",
"V183",
"V327",
"V360",
"V388",
"V396",
"V440",
"V480",
"V591",
"V673",
"V731",
"V754",
"V762",
"V810",
"V884",
"V936",
"V965",
"V995",
"V1040",
"V1059",
"V1081",
"V1083",
"V1116",
"V1203",
"V1240",
"V1250",
"V1266",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,583 |
363,393 | predictive_accuracy | accuracy_score | SOCC | SOCC
SFU Opinion and Comments Corpus
The SFU Opinion and Comments Corpus (SOCC) is a corpus for the analysis of online news comments. Our corpus contains comments and the articles from which the comments originated. The articles are all opinion articles, not hard news articles. The corpus is larger than any other curr... | {0: [0 - article_id (numeric)],
1: [1 - comment_counter (string)],
2: [2 - title (string)],
3: [3 - globe_url (string)],
4: [4 - url (string)],
5: [5 - comment_text (string)],
6: [6 - is_constructive (string)],
7: [7 - is_constructive:confidence (numeric)],
8: [8 - toxicity_level (string)],
9: [9 - toxicity_le... | {'MajorityClassSize': 389.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 14.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 1043.0,
'NumberOfInstancesWithMissingValues': 983.0,
'NumberOfMissingValues': 2641.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.... | SOCC | [
"article_id",
"comment_counter",
"title",
"globe_url",
"url",
"comment_text",
"is_constructive",
"is_constructive:confidence",
"toxicity_level:confidence",
"did_you_read_the_article",
"did_you_read_the_article:confidence",
"annotator_comments",
"expert_is_constructive",
"expert_toxicity_le... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,584 |
363,170 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_MED_LF_Extended | Healthy Medicinal Leaf | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 122.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 56.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 1395.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'... | Stylized_Meta_Album_MED_LF_Extended | [
"FILE_NAME"
] | [
false
] | 3,585 |
363,194 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_TEX_DTD_Extended | Textures dataset from Describable Textures Dataset | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 120.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 120.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 2400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
... | Stylized_Meta_Album_TEX_DTD_Extended | [
"FILE_NAME"
] | [
false
] | 3,587 |
363,179 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_PLT_NET_Mini | Plants Dataset with different species of plants | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_PLT_NET_Mini | [
"FILE_NAME"
] | [
false
] | 3,588 |
363,398 | predictive_accuracy | accuracy_score | QSAR_Bioconcentration_classification | the QSAR Bioconcentration Classes Dataset is a well-known dataset used in cheminformatics and environmental chemistry. It is available from the UCI Machine Learning Repository and is often used for classification and regression tasks related to predicting the bioconcentration factor (BCF) of chemical compounds.
Datase... | {0: [0 - CAS (string)],
1: [1 - SMILES (string)],
2: [2 - Set (string)],
3: [3 - nHM (numeric)],
4: [4 - piPC09 (numeric)],
5: [5 - PCD (numeric)],
6: [6 - X2Av (numeric)],
7: [7 - MLOGP (numeric)],
8: [8 - ON1V (numeric)],
9: [9 - N-072 (numeric)],
10: [10 - B02[C-N] (numeric)],
11: [11 - F04[C-O] (numeric)... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 779.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | QSAR_Bioconcentration_classification | [
"CAS",
"SMILES",
"Set",
"nHM",
"piPC09",
"PCD",
"X2Av",
"MLOGP",
"ON1V",
"N-072",
"B02[C-N]",
"F04[C-O]"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,589 |
363,287 | predictive_accuracy | accuracy_score | dataset_credit-g | Financial dataset for automl benchmark. Name = dataset_credit-g, target = class | {0: [0 - checking_status (numeric)],
1: [1 - duration (numeric)],
2: [2 - credit_history (numeric)],
3: [3 - purpose (numeric)],
4: [4 - credit_amount (numeric)],
5: [5 - savings_status (numeric)],
6: [6 - employment (numeric)],
7: [7 - installment_commitment (numeric)],
8: [8 - personal_status (numeric)],
9: ... | {'MajorityClassSize': 700.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 300.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 1.0,
... | dataset_credit-g | [
"checking_status",
"duration",
"credit_history",
"purpose",
"credit_amount",
"savings_status",
"employment",
"installment_commitment",
"personal_status",
"other_parties",
"residence_since",
"property_magnitude",
"age",
"other_payment_plans",
"housing",
"existing_credits",
"job",
"n... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,590 |
363,183 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_RESISC_Mini | Remote sensing dataset | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_RESISC_Mini | [
"FILE_NAME"
] | [
false
] | 3,591 |
363,578 | predictive_accuracy | accuracy_score | diabetes_risk_prediction_dataset | Dataset Description: Early Stage Diabetes Risk Prediction
This dataset comprises crucial sign and symptom data of individuals who either exhibit early signs of diabetes or are at risk of developing diabetes. The variables included in the dataset provide valuable insights into potential indicators of diabetes onset. Th... | {0: [0 - Age (numeric)],
1: [1 - Gender (string)],
2: [2 - Polyuria (string)],
3: [3 - Polydipsia (string)],
4: [4 - sudden weight loss (string)],
5: [5 - weakness (string)],
6: [6 - Polyphagia (string)],
7: [7 - Genital thrush (string)],
8: [8 - visual blurring (string)],
9: [9 - Itching (string)],
10: [10 -... | {'MajorityClassSize': 320.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 520.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 1.0,
'... | diabetes_risk_prediction_dataset | [
"Age",
"Gender",
"Polyuria",
"Polydipsia",
"sudden weight loss",
"weakness",
"Polyphagia",
"Genital thrush",
"visual blurring",
"Itching",
"Irritability",
"delayed healing",
"partial paresis",
"muscle stiffness",
"Alopecia",
"Obesity"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,592 |
363,458 | predictive_accuracy | accuracy_score | mental_health_detection | Mental Health Detection Dataset
Make your machine learning model more accurate with these dataset
This dataset contains survey responses from patients regarding various symptoms associated with depression. Each patient answered 14 questions, and the responses are coded from 1 to 6 based on the frequency of experienc... | {0: [0 - Number (numeric)],
1: [1 - Sleep (numeric)],
2: [2 - Appetite (numeric)],
3: [3 - Interest (numeric)],
4: [4 - Fatigue (numeric)],
5: [5 - Worthlessness (numeric)],
6: [6 - Concentration (numeric)],
7: [7 - Agitation (numeric)],
8: [8 - Suicidal Ideation (numeric)],
9: [9 - Sleep Disturbance (numeric)... | {'MajorityClassSize': 157.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 540.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 0.0,
'... | mental_health_detection | [
"Number ",
"Sleep",
"Appetite",
"Interest",
"Fatigue",
"Worthlessness",
"Concentration",
"Agitation",
"Suicidal Ideation",
"Sleep Disturbance",
"Aggression",
"Panic Attacks",
"Hopelessness",
"Restlessness",
"Low Energy"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,593 |
363,415 | predictive_accuracy | accuracy_score | bone_marrow_transplant_children | Dataset Information
Additional Information
The data set describes pediatric patients with several hematologic diseases: malignant disorders (i.a. acute lymphoblastic leukemia, acute myelogenous leukemia, chronic myelogenous leukemia, myelodysplastic syndrome) and nonmalignant cases (i.a. severe aplastic anemia, Fancon... | {0: [0 - Recipientgender (numeric)],
1: [1 - Stemcellsource (numeric)],
2: [2 - Donorage (numeric)],
3: [3 - Donorage35 (numeric)],
4: [4 - IIIV (numeric)],
5: [5 - Gendermatch (numeric)],
6: [6 - DonorABO (numeric)],
7: [7 - RecipientABO (numeric)],
8: [8 - RecipientRh (numeric)],
9: [9 - ABOmatch (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 187.0,
'NumberOfInstancesWithMissingValues': 45.0,
'NumberOfMissingValues': 81.0,
'NumberOfNumericFeatures': 36.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | bone_marrow_transplant_children | [
"Recipientgender",
"Stemcellsource",
"Donorage",
"Donorage35",
"IIIV",
"Gendermatch",
"DonorABO",
"RecipientABO",
"RecipientRh",
"ABOmatch",
"CMVstatus",
"DonorCMV",
"RecipientCMV",
"Disease",
"Riskgroup",
"Txpostrelapse",
"Diseasegroup",
"HLAmatch",
"HLAmismatch",
"Antigen",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,594 |
363,195 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_TEX_DTD_Mini | Textures dataset from Describable Textures Dataset | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_TEX_DTD_Mini | [
"FILE_NAME"
] | [
false
] | 3,595 |
363,408 | predictive_accuracy | accuracy_score | regensburg_pediatric_appendicitis | This dataset was acquired in a retrospective study from a cohort of pediatric patients admitted with abdominal pain to Children's Hospital St. Hedwig in Regensburg, Germany. Multiple abdominal B-mode ultrasound images were acquired for most patients, with the number of views varying from 1 to 15. The images depict vari... | {0: [0 - Age (numeric)],
1: [1 - BMI (numeric)],
2: [2 - Sex (string)],
3: [3 - Height (numeric)],
4: [4 - Weight (numeric)],
5: [5 - Length_of_Stay (numeric)],
6: [6 - Alvarado_Score (numeric)],
7: [7 - Paedriatic_Appendicitis_Score (numeric)],
8: [8 - Appendix_on_US (string)],
9: [9 - Appendix_Diameter (nume... | {'MajorityClassSize': 483.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 56.0,
'NumberOfInstances': 782.0,
'NumberOfInstancesWithMissingValues': 782.0,
'NumberOfMissingValues': 13984.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 0.... | regensburg_pediatric_appendicitis | [
"Age",
"BMI",
"Sex",
"Height",
"Weight",
"Length_of_Stay",
"Alvarado_Score",
"Paedriatic_Appendicitis_Score",
"Appendix_on_US",
"Appendix_Diameter",
"Migratory_Pain",
"Lower_Right_Abd_Pain",
"Contralateral_Rebound_Tenderness",
"Coughing_Pain",
"Nausea",
"Loss_of_Appetite",
"Body_Temp... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,596 |
363,187 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_RSICB_Mini | Remote sensing dataset | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_RSICB_Mini | [
"FILE_NAME"
] | [
false
] | 3,597 |
363,607 | predictive_accuracy | accuracy_score | Student_Performance_on_an_Entrance_Examination | Performance in Common Entrance Examination (CEE), Sex of the Candidate, Caste of the Candidate, Whether the candidate attended any coaching classes within Assam, outside Assam or not, Name of the board where the candidate studied at Class X level, Name of the board where the candidate studied at Class XII level, Medium... | {0: [0 - Gender (string)],
1: [1 - Caste (string)],
2: [2 - coaching (string)],
3: [3 - Class_ten_education (string)],
4: [4 - twelve_education (string)],
5: [5 - medium (string)],
6: [6 - Class_X_Percentage (string)],
7: [7 - Class_XII_Percentage (string)],
8: [8 - Father_occupation (string)],
9: [9 - Mother_... | {'MajorityClassSize': 210.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 101.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 666.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 1.0,
'... | Student_Performance_on_an_Entrance_Examination | [
"Gender",
"Caste",
"coaching",
"Class_ten_education",
"twelve_education",
"medium",
"Class_X_Percentage",
"Class_XII_Percentage",
"Father_occupation",
"Mother_occupation",
"time"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,598 |
363,542 | predictive_accuracy | accuracy_score | SOCC | SOCC
SFU Opinion and Comments Corpus
The SFU Opinion and Comments Corpus (SOCC) is a corpus for the analysis of online news comments. Our corpus contains comments and the articles from which the comments originated. The articles are all opinion articles, not hard news articles. The corpus is larger than any other curr... | {0: [0 - title (string)],
1: [1 - globe_url (string)],
2: [2 - url (string)],
3: [3 - comment_text (string)],
4: [4 - is_constructive (string)],
5: [5 - is_constructive_confidence (numeric)],
6: [6 - toxicity_level (nominal)],
7: [7 - toxicity_level_confidence (string)],
8: [8 - did_you_read_the_article (numeri... | {'MajorityClassSize': 829.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 7.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 1043.0,
'NumberOfInstancesWithMissingValues': 983.0,
'NumberOfMissingValues': 2641.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0... | SOCC | [
"title",
"globe_url",
"url",
"comment_text",
"is_constructive",
"is_constructive_confidence",
"toxicity_level_confidence",
"did_you_read_the_article",
"did_you_read_the_article_confidence",
"annotator_comments",
"expert_is_constructive",
"expert_toxicity_level",
"expert_comments"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,599 |
363,295 | predictive_accuracy | accuracy_score | tiniest-imagenet-200 | Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64 x 64 colored images. !!! This dataset only links to 20 images per class (instead of the usual 500) and is ONLY for quickly testing a framework. !!! Each class has 500 training images, 50 validation images, and 50 test images. The d... | {0: [0 - image_path (string)], 1: [1 - label (nominal)]} | {'MajorityClassSize': 20.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 20.0,
'NumberOfClasses': 200.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 4000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 1.0,
'... | tiniest-imagenet-200 | [
"image_path"
] | [
false
] | 3,600 |
363,587 | predictive_accuracy | accuracy_score | PostPartum_Depression | PostPartum Depression
Mental Disorder of pregnant women
About Dataset
In our research, we gathered a dataset of 1503 records from a medical hospital using a
questionnaire administered through a Google form. This dataset has not yet been published.
Our dataset includes 15 attributes, where I select 10 attributes, 9 of... | {0: [0 - Age (string)],
1: [1 - Feeling sad or Tearful (string)],
2: [2 - Irritable towards baby & partner (string)],
3: [3 - Trouble sleeping at night (string)],
4: [4 - Problems concentrating or making decision (string)],
5: [5 - Overeating or loss of appetite (string)],
6: [6 - Feeling_anxious (nominal)],
7: ... | {'MajorityClassSize': 980.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 523.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1503.0,
'NumberOfInstancesWithMissingValues': 12.0,
'NumberOfMissingValues': 27.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 1.0,... | PostPartum_Depression | [
"Age",
"Feeling sad or Tearful",
"Irritable towards baby & partner",
"Trouble sleeping at night",
"Problems concentrating or making decision",
"Overeating or loss of appetite",
"Feeling of guilt",
"Problems of bonding with baby",
"Suicide attempt"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,601 |
363,409 | predictive_accuracy | accuracy_score | glioma_grading_clinical_and_mutation_features | For what purpose was the dataset created?
Gliomas are the most common primary tumors of the brain. They can be graded as LGG (Lower-Grade Glioma) or GBM (Glioblastoma Multiforme) depending on the histological/imaging criteria. Clinical and molecular/mutation factors are also very crucial for the grading process. Molec... | {0: [0 - Gender (numeric)],
1: [1 - Age_at_diagnosis (numeric)],
2: [2 - Race (string)],
3: [3 - IDH1 (numeric)],
4: [4 - TP53 (numeric)],
5: [5 - ATRX (numeric)],
6: [6 - PTEN (numeric)],
7: [7 - EGFR (numeric)],
8: [8 - CIC (numeric)],
9: [9 - MUC16 (numeric)],
10: [10 - PIK3CA (numeric)],
11: [11 - NF1 (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 24.0,
'NumberOfInstances': 839.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 23.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | glioma_grading_clinical_and_mutation_features | [
"Gender",
"Age_at_diagnosis",
"Race",
"IDH1",
"TP53",
"ATRX",
"PTEN",
"EGFR",
"CIC",
"MUC16",
"PIK3CA",
"NF1",
"PIK3R1",
"FUBP1",
"RB1",
"NOTCH1",
"BCOR",
"CSMD3",
"SMARCA4",
"GRIN2A",
"IDH2",
"FAT4",
"PDGFRA"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,602 |
363,589 | predictive_accuracy | accuracy_score | mental_health_detection | Mental Health Detection Dataset
Make your machine learning model more accurate with these dataset
This dataset contains survey responses from patients regarding various symptoms associated with depression. Each patient answered 14 questions, and the responses are coded from 1 to 6 based on the frequency of experienc... | {0: [0 - Sleep (numeric)],
1: [1 - Appetite (numeric)],
2: [2 - Interest (numeric)],
3: [3 - Fatigue (numeric)],
4: [4 - Worthlessness (numeric)],
5: [5 - Concentration (numeric)],
6: [6 - Agitation (numeric)],
7: [7 - Suicidal Ideation (numeric)],
8: [8 - Sleep Disturbance (numeric)],
9: [9 - Aggression (nume... | {'MajorityClassSize': 174.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 118.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 540.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 1.0,
... | mental_health_detection | [
"Sleep",
"Appetite",
"Interest",
"Fatigue",
"Worthlessness",
"Concentration",
"Agitation",
"Suicidal Ideation",
"Sleep Disturbance",
"Aggression",
"Panic Attacks",
"Hopelessness",
"Restlessness",
"Low Energy"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,603 |
363,191 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_SPT_Mini | 100 Sports Dataset | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_SPT_Mini | [
"FILE_NAME"
] | [
false
] | 3,604 |
363,609 | predictive_accuracy | accuracy_score | TESTFORDESCRIPTION-anneal | TEST DATASET FOR THE DESCRIPTION AS THIS DOES NOT WORK ON THE TEST SERVER.
PLEASE IGNORE. | {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': nan,
'MinorityClassSize': 8.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 898.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 33.0,
'c... | TESTFORDESCRIPTION-anneal | [
"family",
"product-type",
"steel",
"carbon",
"hardness",
"temper_rolling",
"condition",
"formability",
"strength",
"non-ageing",
"surface-finish",
"surface-quality",
"enamelability",
"bc",
"bf",
"bt",
"bw_me",
"bl",
"m",
"chrom",
"phos",
"cbond",
"marvi",
"exptl",
"fe... | [
true,
true,
true,
false,
false,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
true,
true,
true
] | 3,605 |
363,476 | predictive_accuracy | accuracy_score | bredel-2005 | From original source:
-----
The authors applied refined network knowledge to the analysis of key functions and pathways associated with gliomagenesis in a set of 50 human gliomas. For the analysis of normal brain versus glioma subtypes, the authors grouped tumors into 31 pure glioblastomas (GBM) and 14 tumors with enr... | {0: [0 - 83 (numeric)],
1: [1 - 164 (numeric)],
2: [2 - 184 (numeric)],
3: [3 - 221 (numeric)],
4: [4 - 233 (numeric)],
5: [5 - 326 (numeric)],
6: [6 - 336 (numeric)],
7: [7 - 343 (numeric)],
8: [8 - 348 (numeric)],
9: [9 - 355 (numeric)],
10: [10 - 358 (numeric)],
11: [11 - 387 (numeric)],
12: [12 - 390 (n... | {'MajorityClassSize': 31.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 1740.0,
'NumberOfInstances': 50.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1739.0,
'NumberOfSymbolicFeatures': 0.0,
... | bredel-2005 | [
"83",
"164",
"184",
"221",
"233",
"326",
"336",
"343",
"348",
"355",
"358",
"387",
"390",
"393",
"452",
"490",
"508",
"526",
"533",
"550",
"596",
"618",
"676",
"682",
"683",
"718",
"725",
"736",
"767",
"782",
"790",
"854",
"942",
"960",
"961",
"9... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,606 |
363,454 | predictive_accuracy | accuracy_score | divorce_prediction | Divorce Prediction
About Dataset
Abstract
Ever been heart broken and/or wondered what makes a lasting relationship? This dataset may help you.
About this dataset
This dataset contains data about 150 couples with their corresponding Divorce Predictors Scale variables (DPS) on the basis of Gottman couples therapy.
The ... | {0: [0 - Q1 (numeric)],
1: [1 - Q2 (numeric)],
2: [2 - Q3 (numeric)],
3: [3 - Q4 (numeric)],
4: [4 - Q5 (numeric)],
5: [5 - Q6 (numeric)],
6: [6 - Q7 (numeric)],
7: [7 - Q8 (numeric)],
8: [8 - Q9 (numeric)],
9: [9 - Q10 (numeric)],
10: [10 - Q11 (numeric)],
11: [11 - Q12 (numeric)],
12: [12 - Q13 (numeric)]... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 55.0,
'NumberOfInstances': 170.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 55.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | divorce_prediction | [
"Q1",
"Q2",
"Q3",
"Q4",
"Q5",
"Q6",
"Q7",
"Q8",
"Q9",
"Q10",
"Q11",
"Q12",
"Q13",
"Q14",
"Q15",
"Q16",
"Q17",
"Q18",
"Q19",
"Q20",
"Q21",
"Q22",
"Q23",
"Q24",
"Q25",
"Q26",
"Q27",
"Q28",
"Q29",
"Q30",
"Q31",
"Q32",
"Q33",
"Q34",
"Q35",
"Q36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,607 |
363,634 | predictive_accuracy | accuracy_score | Fitness_Club | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - months_as_member (numeric)],
1: [1 - weight (numeric)],
2: [2 - days_before (numeric)],
3: [3 - day_of_week (nominal)],
4: [4 - time (nominal)],
5: [5 - category (nominal)],
6: [6 - attended (nominal)]} | {'MajorityClassSize': 1046.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 454.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1500.0,
'NumberOfInstancesWithMissingValues': 20.0,
'NumberOfMissingValues': 20.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 4.0,... | Fitness_Club | [
"months_as_member",
"weight",
"days_before",
"day_of_week",
"time",
"category"
] | [
false,
false,
false,
true,
true,
true
] | 3,608 |
363,258 | predictive_accuracy | accuracy_score | meta_features | Meta features classified with best models | {0: [0 - DatasetRatio (numeric)],
1: [1 - InverseDatasetRatio (numeric)],
2: [2 - KurtosisMax (numeric)],
3: [3 - KurtosisMean (numeric)],
4: [4 - KurtosisMin (numeric)],
5: [5 - KurtosisSTD (numeric)],
6: [6 - LogDatasetRatio (numeric)],
7: [7 - LogInverseDatasetRatio (numeric)],
8: [8 - LogNumberOfFeatures (n... | {'MajorityClassSize': 22.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 32.0,
'NumberOfInstances': 75.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 31.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | meta_features | [
"DatasetRatio",
"InverseDatasetRatio",
"KurtosisMax",
"KurtosisMean",
"KurtosisMin",
"KurtosisSTD",
"LogDatasetRatio",
"LogInverseDatasetRatio",
"LogNumberOfFeatures",
"LogNumberOfInstances",
"NumberOfCategoricalFeatures",
"NumberOfFeatures",
"NumberOfFeaturesWithMissingValues",
"NumberOfI... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,609 |
363,400 | predictive_accuracy | accuracy_score | Cervical_Cancer_Risk_Factors | Cervical cancer (Risk Factors) Data Set (multilabel classification)
The dataset was collected at 'Hospital Universitario de Caracas' in Caracas, Venezuela. The dataset comprises demographic information, habits, and historic medical records of 858 patients. Several patients decided not to answer some of the questions b... | {0: [0 - Age (numeric)],
1: [1 - Number of sexual partners (numeric)],
2: [2 - First sexual intercourse (numeric)],
3: [3 - Num of pregnancies (numeric)],
4: [4 - Smokes (numeric)],
5: [5 - Smokes (years) (numeric)],
6: [6 - Smokes (packs/year) (numeric)],
7: [7 - Hormonal Contraceptives (numeric)],
8: [8 - Hor... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 858.0,
'NumberOfInstancesWithMissingValues': 799.0,
'NumberOfMissingValues': 3622.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
... | Cervical_Cancer_Risk_Factors | [
"Age",
"Number of sexual partners",
"First sexual intercourse",
"Num of pregnancies",
"Smokes",
"Smokes (years)",
"Smokes (packs/year)",
"Hormonal Contraceptives",
"Hormonal Contraceptives (years)",
"IUD",
"IUD (years)",
"STDs",
"STDs (number)",
"STDs:condylomatosis",
"STDs:cervical cond... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,610 |
363,575 | predictive_accuracy | accuracy_score | OSMI_Mental_Health_in_Tech_Survey | About Dataset
OSMI Mental Health in Tech Survey 2016
Currently over 1400 responses, the ongoing 2016 survey aims to measure attitudes towards mental health in the tech workplace, and examine the frequency of mental health disorders among tech workers.
How Will This Data Be Used?
We are interested in gauging how mental... | {0: [0 - self_employed (numeric)],
1: [1 - num_employees (string)],
2: [2 - employer_tech (numeric)],
3: [3 - role_tech_related (numeric)],
4: [4 - mental_health_benefits (string)],
5: [5 - know_mental_health_options (string)],
6: [6 - employer_discussed_mental_health (string)],
7: [7 - employer_mental_health_re... | {'MajorityClassSize': 757.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 333.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 63.0,
'NumberOfInstances': 1433.0,
'NumberOfInstancesWithMissingValues': 1433.0,
'NumberOfMissingValues': 21961.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures':... | OSMI_Mental_Health_in_Tech_Survey | [
"self_employed",
"num_employees",
"employer_tech",
"role_tech_related",
"mental_health_benefits",
"know_mental_health_options",
"employer_discussed_mental_health",
"employer_mental_health_resources",
"anonymity_protected",
"medical_leave_mental_health",
"discuss_mental_negative",
"discuss_phys... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,612 |
363,254 | predictive_accuracy | accuracy_score | Automl_meta_feat | Automl_meta_feat | {0: [0 - DatasetRatio (numeric)],
1: [1 - InverseDatasetRatio (numeric)],
2: [2 - KurtosisMax (numeric)],
3: [3 - KurtosisMean (numeric)],
4: [4 - KurtosisMin (numeric)],
5: [5 - KurtosisSTD (numeric)],
6: [6 - LogDatasetRatio (numeric)],
7: [7 - LogInverseDatasetRatio (numeric)],
8: [8 - LogNumberOfFeatures (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 32.0,
'NumberOfInstances': 75.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 31.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Automl_meta_feat | [
"DatasetRatio",
"InverseDatasetRatio",
"KurtosisMax",
"KurtosisMean",
"KurtosisMin",
"KurtosisSTD",
"LogDatasetRatio",
"LogInverseDatasetRatio",
"LogNumberOfFeatures",
"LogNumberOfInstances",
"NumberOfCategoricalFeatures",
"NumberOfFeatures",
"NumberOfFeaturesWithMissingValues",
"NumberOfI... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,613 |
363,657 | predictive_accuracy | accuracy_score | Pumpkin_Seeds | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - Area (numeric)],
1: [1 - Perimeter (numeric)],
2: [2 - Major_Axis_Length (numeric)],
3: [3 - Minor_Axis_Length (numeric)],
4: [4 - Convex_Area (numeric)],
5: [5 - Equiv_Diameter (numeric)],
6: [6 - Eccentricity (numeric)],
7: [7 - Solidity (numeric)],
8: [8 - Extent (numeric)],
9: [9 - Roundness (nume... | {'MajorityClassSize': 1300.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1200.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 2500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 1.0... | Pumpkin_Seeds | [
"Area",
"Perimeter",
"Major_Axis_Length",
"Minor_Axis_Length",
"Convex_Area",
"Equiv_Diameter",
"Eccentricity",
"Solidity",
"Extent",
"Roundness",
"Aspect_Ration",
"Compactness"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,614 |
363,474 | predictive_accuracy | accuracy_score | armstrong-2002-v1 | From original source:
-----
The authors showed that clustering algorithms reveal that lymphoblastic leukemias with MLL translocations (MLL) can clearly be separated from conventional acute lymphoblastic (ALL) and acute myelogenous leukemias (AML).
From these, we formed two data sets with the following distribution o... | {0: [0 - AFFX-PheX-3_at (numeric)],
1: [1 - AFFX-HUMISGF3A/M97935_5_at (numeric)],
2: [2 - AFFX-HUMRGE/M10098_5_at (numeric)],
3: [3 - AFFX-HUMRGE/M10098_M_at (numeric)],
4: [4 - AFFX-HUMTFRR/M11507_M_at (numeric)],
5: [5 - AFFX-M27830_5_at (numeric)],
6: [6 - 31312_at (numeric)],
7: [7 - 31314_at (numeric)],
8... | {'MajorityClassSize': 48.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 24.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1082.0,
'NumberOfInstances': 72.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1081.0,
'NumberOfSymbolicFeatures': 0.0,
... | armstrong-2002-v1 | [
"AFFX-PheX-3_at",
"AFFX-HUMISGF3A/M97935_5_at",
"AFFX-HUMRGE/M10098_5_at",
"AFFX-HUMRGE/M10098_M_at",
"AFFX-HUMTFRR/M11507_M_at",
"AFFX-M27830_5_at",
"31312_at",
"31314_at",
"31315_at",
"31319_at",
"31341_at",
"31362_at",
"31387_at",
"31404_at",
"31414_at",
"31416_at",
"31419_r_at",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,615 |
363,424 | predictive_accuracy | accuracy_score | Fetal_cardiotocography_dataset | 2126 fetal cardiotocograms (CTGs) were automatically processed and the respective diagnostic features measured. The CTGs were also classified by three expert obstetricians and a consensus classification label assigned to each of them. Classification was both with respect to a morphologic pattern (A, B, C. ...) and to a... | {0: [0 - FileName (string)],
1: [1 - Date (string)],
2: [2 - SegFile (string)],
3: [3 - b (numeric)],
4: [4 - e (numeric)],
5: [5 - LBE (numeric)],
6: [6 - LB (numeric)],
7: [7 - AC (numeric)],
8: [8 - FM (numeric)],
9: [9 - UC (numeric)],
10: [10 - ASTV (numeric)],
11: [11 - MSTV (numeric)],
12: [12 - ALTV... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 40.0,
'NumberOfInstances': 2126.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 37.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Fetal_cardiotocography_dataset | [
"FileName",
"Date",
"SegFile",
"b",
"e",
"LBE",
"LB",
"AC",
"FM",
"UC",
"ASTV",
"MSTV",
"ALTV",
"MLTV",
"DL",
"DS",
"DP",
"DR",
"Width",
"Min",
"Max",
"Nmax",
"Nzeros",
"Mode",
"Mean",
"Median",
"Variance",
"Tendency",
"A",
"B",
"C",
"D",
"E",
"AD",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,616 |
363,651 | predictive_accuracy | accuracy_score | Mobile_Price | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - battery_power (numeric)],
1: [1 - blue (nominal)],
2: [2 - clock_speed (numeric)],
3: [3 - dual_sim (nominal)],
4: [4 - fc (numeric)],
5: [5 - four_g (nominal)],
6: [6 - int_memory (numeric)],
7: [7 - m_dep (numeric)],
8: [8 - mobile_wt (numeric)],
9: [9 - n_cores (numeric)],
10: [10 - pc (numeric)],... | {'MajorityClassSize': 500.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 500.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 7.0,
... | Mobile_Price | [
"battery_power",
"blue",
"clock_speed",
"dual_sim",
"fc",
"four_g",
"int_memory",
"m_dep",
"mobile_wt",
"n_cores",
"pc",
"px_height",
"px_width",
"ram",
"sc_h",
"sc_w",
"talk_time",
"three_g",
"touch_screen",
"wifi"
] | [
false,
true,
false,
true,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true
] | 3,618 |
363,412 | predictive_accuracy | accuracy_score | hepatitis_c_virus_hcv_for_egyptian_patients | Egyptian patients who underwent treatment dosages for HCV about 18 months. Discretization should be applied based on expert recommendations; there is an attached file shows how.
Age Age
Gender Gender
BMI Body Mass Index
Fever Fever
Nausea/Vomting Nausea/Vomting
Headache Headache
Diarrhea Diarrhea
Fatigue & generalized... | {0: [0 - Age_ (numeric)],
1: [1 - Gender (numeric)],
2: [2 - BMI (numeric)],
3: [3 - Fever (numeric)],
4: [4 - Nausea/Vomting (numeric)],
5: [5 - Headache_ (numeric)],
6: [6 - Diarrhea_ (numeric)],
7: [7 - Fatigue_&_generalized_bone_ache_ (numeric)],
8: [8 - Jaundice_ (numeric)],
9: [9 - Epigastric_pain_ (nume... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 29.0,
'NumberOfInstances': 1385.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 29.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | hepatitis_c_virus_hcv_for_egyptian_patients | [
"Age_",
"Gender",
"BMI",
"Fever",
"Nausea/Vomting",
"Headache_",
"Diarrhea_",
"Fatigue_&_generalized_bone_ache_",
"Jaundice_",
"Epigastric_pain_",
"WBC",
"RBC",
"HGB",
"Plat",
"AST_1",
"ALT_1",
"ALT4",
"ALT_12",
"ALT_24",
"ALT_36",
"ALT_48",
"ALT_after_24_w",
"RNA_Base",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,619 |
363,159 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_BRD_Extended | Birds dataset for image classification | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 273.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 213.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 4531.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
... | Stylized_Meta_Album_BRD_Extended | [
"FILE_NAME"
] | [
false
] | 3,620 |
363,664 | predictive_accuracy | accuracy_score | splice | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - position_-30 (nominal)],
1: [1 - position_-29 (nominal)],
2: [2 - position_-28 (nominal)],
3: [3 - position_-27 (nominal)],
4: [4 - position_-26 (nominal)],
5: [5 - position_-25 (nominal)],
6: [6 - position_-24 (nominal)],
7: [7 - position_-23 (nominal)],
8: [8 - position_-22 (nominal)],
9: [9 - posit... | {'MajorityClassSize': 1655.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 767.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 3190.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 61.0,... | splice | [
"position_-30",
"position_-29",
"position_-28",
"position_-27",
"position_-26",
"position_-25",
"position_-24",
"position_-23",
"position_-22",
"position_-21",
"position_-20",
"position_-19",
"position_-18",
"position_-17",
"position_-16",
"position_-15",
"position_-14",
"position_... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,623 |
363,190 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_SPT_Extended | 100 Sports Dataset | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 201.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 161.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 3511.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
... | Stylized_Meta_Album_SPT_Extended | [
"FILE_NAME"
] | [
false
] | 3,624 |
363,357 | predictive_accuracy | accuracy_score | AVIDa-SARS-CoV-2 | AVIDa-SARS-CoV-2 is a dataset featuring the antigen-variable domain of heavy
chain of heavy chain antibody (VHH) interactions obtained from two alpacas
immunized with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
spike proteins. AVIDa-SARS-CoV-2 includes binary labels indicating the binding
or non-bi... | {0: [0 - VHH_sequence (string)],
1: [1 - Ag_label (string)],
2: [2 - label (numeric)],
3: [3 - subject_species (string)],
4: [4 - subject_name (string)],
5: [5 - subject_sex (string)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 3733.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | AVIDa-SARS-CoV-2 | [
"VHH_sequence",
"Ag_label",
"subject_species",
"subject_name",
"subject_sex"
] | [
false,
false,
false,
false,
false
] | 3,625 |
363,256 | predictive_accuracy | accuracy_score | metafeatures | meta features with best model | {0: [0 - DatasetRatio (numeric)],
1: [1 - InverseDatasetRatio (numeric)],
2: [2 - KurtosisMax (numeric)],
3: [3 - KurtosisMean (numeric)],
4: [4 - KurtosisMin (numeric)],
5: [5 - KurtosisSTD (numeric)],
6: [6 - LogDatasetRatio (numeric)],
7: [7 - LogInverseDatasetRatio (numeric)],
8: [8 - LogNumberOfFeatures (n... | {'MajorityClassSize': 22.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 32.0,
'NumberOfInstances': 75.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 31.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | metafeatures | [
"DatasetRatio",
"InverseDatasetRatio",
"KurtosisMax",
"KurtosisMean",
"KurtosisMin",
"KurtosisSTD",
"LogDatasetRatio",
"LogInverseDatasetRatio",
"LogNumberOfFeatures",
"LogNumberOfInstances",
"NumberOfCategoricalFeatures",
"NumberOfFeatures",
"NumberOfFeaturesWithMissingValues",
"NumberOfI... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,626 |
363,064 | predictive_accuracy | accuracy_score | Colon | **Colon dataset**
**Authors**: U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra, D. Mack, A. Levine
**Please cite**: ([URL](https://www.pnas.org/doi/abs/10.1073/pnas.96.12.6745)): U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra, D. Mack, A. Levine, Broad patterns of gene expression revealed by clustering ana... | {0: [0 - att_1 (numeric)],
1: [1 - att_2 (numeric)],
2: [2 - att_3 (numeric)],
3: [3 - att_4 (numeric)],
4: [4 - att_5 (numeric)],
5: [5 - att_6 (numeric)],
6: [6 - att_7 (numeric)],
7: [7 - att_8 (numeric)],
8: [8 - att_9 (numeric)],
9: [9 - att_10 (numeric)],
10: [10 - att_11 (numeric)],
11: [11 - att_12 (... | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 22.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 2001.0,
'NumberOfInstances': 62.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2000.0,
'NumberOfSymbolicFeatures': 0.0,
... | Colon | [
"att_1",
"att_2",
"att_3",
"att_4",
"att_5",
"att_6",
"att_7",
"att_8",
"att_9",
"att_10",
"att_11",
"att_12",
"att_13",
"att_14",
"att_15",
"att_16",
"att_17",
"att_18",
"att_19",
"att_20",
"att_21",
"att_22",
"att_23",
"att_24",
"att_25",
"att_26",
"att_27",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,627 |
363,257 | predictive_accuracy | accuracy_score | meta_features | Meta features classified with best models | {0: [0 - DatasetRatio (numeric)],
1: [1 - InverseDatasetRatio (numeric)],
2: [2 - KurtosisMax (numeric)],
3: [3 - KurtosisMean (numeric)],
4: [4 - KurtosisMin (numeric)],
5: [5 - KurtosisSTD (numeric)],
6: [6 - LogDatasetRatio (numeric)],
7: [7 - LogInverseDatasetRatio (numeric)],
8: [8 - LogNumberOfFeatures (n... | {'MajorityClassSize': 22.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 32.0,
'NumberOfInstances': 75.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 31.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | meta_features | [
"DatasetRatio",
"InverseDatasetRatio",
"KurtosisMax",
"KurtosisMean",
"KurtosisMin",
"KurtosisSTD",
"LogDatasetRatio",
"LogInverseDatasetRatio",
"LogNumberOfFeatures",
"LogNumberOfInstances",
"NumberOfCategoricalFeatures",
"NumberOfFeatures",
"NumberOfFeaturesWithMissingValues",
"NumberOfI... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,628 |
363,362 | predictive_accuracy | accuracy_score | product_sentiment_machine_hack | Classify the sentiment (4-way classification) of user reviews of products based on the review
text and product type (e.g. Tablet, Mobile, etc.). Intuitively, we expect most of the predictive signal to
lie in the text, but predictions can be further improved by accounting for the fact that certain types of
p... | {0: [0 - Unnamed: 0 (numeric)],
1: [1 - Text_ID (numeric)],
2: [2 - Product_Description (string)],
3: [3 - Product_Type (numeric)],
4: [4 - Sentiment (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 5091.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | product_sentiment_machine_hack | [
"Unnamed: 0",
"Text_ID",
"Product_Description",
"Product_Type"
] | [
false,
false,
false,
false
] | 3,629 |
363,555 | predictive_accuracy | accuracy_score | glioma_grading_clinical_and_mutation_features | For what purpose was the dataset created?
Gliomas are the most common primary tumors of the brain. They can be graded as LGG (Lower-Grade Glioma) or GBM (Glioblastoma Multiforme) depending on the histological/imaging criteria. Clinical and molecular/mutation factors are also very crucial for the grading process. Molec... | {0: [0 - Gender (numeric)],
1: [1 - Age_at_diagnosis (numeric)],
2: [2 - Race (string)],
3: [3 - IDH1 (numeric)],
4: [4 - TP53 (numeric)],
5: [5 - ATRX (numeric)],
6: [6 - PTEN (numeric)],
7: [7 - EGFR (numeric)],
8: [8 - CIC (numeric)],
9: [9 - MUC16 (numeric)],
10: [10 - PIK3CA (numeric)],
11: [11 - NF1 (n... | {'MajorityClassSize': 487.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 352.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 24.0,
'NumberOfInstances': 839.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22.0,
'NumberOfSymbolicFeatures': 1.0,
... | glioma_grading_clinical_and_mutation_features | [
"Gender",
"Age_at_diagnosis",
"Race",
"IDH1",
"TP53",
"ATRX",
"PTEN",
"EGFR",
"CIC",
"MUC16",
"PIK3CA",
"NF1",
"PIK3R1",
"FUBP1",
"RB1",
"NOTCH1",
"BCOR",
"CSMD3",
"SMARCA4",
"GRIN2A",
"IDH2",
"FAT4",
"PDGFRA"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,630 |
363,175 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_PLT_DOC_Mini | Plant disease dataset | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_PLT_DOC_Mini | [
"FILE_NAME"
] | [
false
] | 3,631 |
363,455 | predictive_accuracy | accuracy_score | depression_2020 | Context
Hi,
The original Dataset wad published by Frankcc in the following link: Link Kaggle
The dataset is involved into the analysis of depression. The data was consists as a study about the life conditions of people who live in rurales zones. Because all the columns were not explicated in this challenge so We can'... | {0: [0 - Survey_id (numeric)],
1: [1 - Ville_id (numeric)],
2: [2 - sex (numeric)],
3: [3 - Age (numeric)],
4: [4 - Married (numeric)],
5: [5 - Number_children (numeric)],
6: [6 - education_level (numeric)],
7: [7 - total_members (numeric)],
8: [8 - gained_asset (numeric)],
9: [9 - durable_asset (numeric)],
1... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 1429.0,
'NumberOfInstancesWithMissingValues': 20.0,
'NumberOfMissingValues': 20.0,
'NumberOfNumericFeatures': 23.0,
'NumberOfSymbolicFeatures': 0.0,
'... | depression_2020 | [
"Survey_id",
"Ville_id",
"sex",
"Age",
"Married",
"Number_children",
"education_level",
"total_members",
"gained_asset",
"durable_asset",
"save_asset",
"living_expenses",
"other_expenses",
"incoming_salary",
"incoming_own_farm",
"incoming_business",
"incoming_no_business",
"incomin... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,633 |
363,425 | predictive_accuracy | accuracy_score | Skin_Cancer_PAD-UFES-20 | About Dataset
Summary description
Published: 7 Jul 2020
The skin lesions are: Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Actinic Keratosis (ACK), Seborrheic Keratosis (SEK), Bowen's disease (BOD), Melanoma (MEL), and Nevus (NEV). As the Bowen's disease is considered SCC in situ, we clustered them toge... | {0: [0 - patient_id (string)],
1: [1 - lesion_id (numeric)],
2: [2 - smoke (nominal)],
3: [3 - drink (nominal)],
4: [4 - background_father (string)],
5: [5 - background_mother (string)],
6: [6 - age (numeric)],
7: [7 - pesticide (nominal)],
8: [8 - gender (string)],
9: [9 - skin_cancer_history (nominal)],
10:... | {'MajorityClassSize': 1342.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 956.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 2298.0,
'NumberOfInstancesWithMissingValues': 824.0,
'NumberOfMissingValues': 10484.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures':... | Skin_Cancer_PAD-UFES-20 | [
"patient_id",
"lesion_id",
"smoke",
"drink",
"background_father",
"background_mother",
"age",
"pesticide",
"gender",
"skin_cancer_history",
"cancer_history",
"has_piped_water",
"has_sewage_system",
"fitspatrick",
"region",
"diameter_1",
"diameter_2",
"diagnostic",
"itch",
"grew... | [
false,
false,
true,
true,
false,
false,
false,
true,
false,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,634 |
363,279 | predictive_accuracy | accuracy_score | dataset_credit-g | Financial dataset for automl benchmark. Name = dataset_credit-g, target = class | {0: [0 - checking_status (numeric)],
1: [1 - duration (numeric)],
2: [2 - credit_history (numeric)],
3: [3 - purpose (numeric)],
4: [4 - credit_amount (numeric)],
5: [5 - savings_status (numeric)],
6: [6 - employment (numeric)],
7: [7 - installment_commitment (numeric)],
8: [8 - personal_status (numeric)],
9: ... | {'MajorityClassSize': 700.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 300.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 0.0,
... | dataset_credit-g | [
"checking_status",
"duration",
"credit_history",
"purpose",
"credit_amount",
"savings_status",
"employment",
"installment_commitment",
"personal_status",
"other_parties",
"residence_since",
"property_magnitude",
"age",
"other_payment_plans",
"housing",
"existing_credits",
"job",
"n... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,635 |
363,405 | predictive_accuracy | accuracy_score | maternal_health_risk | Additional Information
Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, HeartRate and RiskLevel. All these are the responsible and significant risk factors for maternal mortality, that is one of the main concern of SDG of UN.
Has Missing Values?... | {0: [0 - Age (numeric)],
1: [1 - SystolicBP (numeric)],
2: [2 - DiastolicBP (numeric)],
3: [3 - BS (numeric)],
4: [4 - BodyTemp (numeric)],
5: [5 - HeartRate (numeric)],
6: [6 - RiskLevel (string)]} | {'MajorityClassSize': 406.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 272.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1014.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'... | maternal_health_risk | [
"Age",
"SystolicBP",
"DiastolicBP",
"BS",
"BodyTemp",
"HeartRate"
] | [
false,
false,
false,
false,
false,
false
] | 3,636 |
363,276 | predictive_accuracy | accuracy_score | Corporate_Credit_Rating_Classification | # Credit Ratings | {0: [0 - rating (nominal)],
1: [1 - rating agency name (string)],
2: [2 - sector (string)],
3: [3 - currentratio (numeric)],
4: [4 - quickratio (numeric)],
5: [5 - cashratio (numeric)],
6: [6 - daysofsalesoutstanding (numeric)],
7: [7 - netprofitmargin (numeric)],
8: [8 - pretaxprofitmargin (numeric)],
9: [9 -... | {'MajorityClassSize': 671.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2029.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 28.0,
'NumberOfSymbolicFeatures': 1.0,
... | Corporate_Credit_Rating_Classification | [
"rating agency name",
"sector",
"currentratio",
"quickratio",
"cashratio",
"daysofsalesoutstanding",
"netprofitmargin",
"pretaxprofitmargin",
"grossprofitmargin",
"operatingprofitmargin",
"returnonassets",
"returnoncapitalemployed",
"returnonequity",
"assetturnover",
"fixedassetturnover"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,637 |
363,724 | predictive_accuracy | accuracy_score | Cervical_Cancer_Risk_Factors | Cervical cancer (Risk Factors) Data Set (multilabel classification)
The dataset was collected at 'Hospital Universitario de Caracas' in Caracas, Venezuela. The dataset comprises demographic information, habits, and historic medical records of 858 patients. Several patients decided not to answer some of the questions b... | {0: [0 - Age (numeric)],
1: [1 - Number_of_sexual_partners (numeric)],
2: [2 - First_sexual_intercourse (numeric)],
3: [3 - Num_of_pregnancies (numeric)],
4: [4 - Smokes (numeric)],
5: [5 - Smokes_years (numeric)],
6: [6 - Smokes_packs/year (numeric)],
7: [7 - Hormonal_Contraceptives (numeric)],
8: [8 - Hormona... | {'MajorityClassSize': 756.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 10.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 858.0,
'NumberOfInstancesWithMissingValues': 799.0,
'NumberOfMissingValues': 3622.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.... | Cervical_Cancer_Risk_Factors | [
"Age",
"Number_of_sexual_partners",
"First_sexual_intercourse",
"Num_of_pregnancies",
"Smokes",
"Smokes_years",
"Smokes_packs/year",
"Hormonal_Contraceptives",
"Hormonal_Contraceptives_years",
"IUD",
"IUD_years",
"STDs",
"STDs_number",
"STDs_condylomatosis",
"STDs_cervical_condylomatosis... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,639 |
363,402 | predictive_accuracy | accuracy_score | Student_Performance_on_an_Entrance_Examination | Performance in Common Entrance Examination (CEE), Sex of the Candidate, Caste of the Candidate, Whether the candidate attended any coaching classes within Assam, outside Assam or not, Name of the board where the candidate studied at Class X level, Name of the board where the candidate studied at Class XII level, Medium... | {0: [0 - Gender (string)],
1: [1 - Caste (string)],
2: [2 - coaching (string)],
3: [3 - Class_ten_education (string)],
4: [4 - twelve_education (string)],
5: [5 - medium (string)],
6: [6 - Class_X_Percentage (string)],
7: [7 - Class_XII_Percentage (string)],
8: [8 - Father_occupation (string)],
9: [9 - Mother_... | {'MajorityClassSize': 210.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 101.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 666.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'... | Student_Performance_on_an_Entrance_Examination | [
"Gender",
"Caste",
"coaching",
"Class_ten_education",
"twelve_education",
"medium",
"Class_X_Percentage",
"Class_XII_Percentage",
"Father_occupation",
"Mother_occupation",
"time"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,640 |
363,548 | predictive_accuracy | accuracy_score | Student_Performance_on_an_Entrance_Examination | Performance in Common Entrance Examination (CEE), Sex of the Candidate, Caste of the Candidate, Whether the candidate attended any coaching classes within Assam, outside Assam or not, Name of the board where the candidate studied at Class X level, Name of the board where the candidate studied at Class XII level, Medium... | {0: [0 - Gender (string)],
1: [1 - Caste (string)],
2: [2 - coaching (string)],
3: [3 - Class_ten_education (string)],
4: [4 - twelve_education (string)],
5: [5 - medium (string)],
6: [6 - Class_X_Percentage (string)],
7: [7 - Class_XII_Percentage (string)],
8: [8 - Father_occupation (string)],
9: [9 - Mother_... | {'MajorityClassSize': 210.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 101.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 666.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 1.0,
'... | Student_Performance_on_an_Entrance_Examination | [
"Gender",
"Caste",
"coaching",
"Class_ten_education",
"twelve_education",
"medium",
"Class_X_Percentage",
"Class_XII_Percentage",
"Father_occupation",
"Mother_occupation",
"time"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,641 |
363,277 | predictive_accuracy | accuracy_score | Corporate_Credit_Rating_Classification | # Credit Ratings of Big US Firms and their Financials
## Context
A corporate credit rating expresses the ability of a firm to repay its debt to creditors. Credit rating agencies assess companies' creditworthiness based on financial indicators.
## Content
This dataset contains 2029 credit ratings issued by major agenc... | {0: [0 - rating (nominal)],
1: [1 - rating agency name (string)],
2: [2 - sector (string)],
3: [3 - currentratio (numeric)],
4: [4 - quickratio (numeric)],
5: [5 - cashratio (numeric)],
6: [6 - daysofsalesoutstanding (numeric)],
7: [7 - netprofitmargin (numeric)],
8: [8 - pretaxprofitmargin (numeric)],
9: [9 -... | {'MajorityClassSize': 671.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2029.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 28.0,
'NumberOfSymbolicFeatures': 1.0,
... | Corporate_Credit_Rating_Classification | [
"rating agency name",
"sector",
"currentratio",
"quickratio",
"cashratio",
"daysofsalesoutstanding",
"netprofitmargin",
"pretaxprofitmargin",
"grossprofitmargin",
"operatingprofitmargin",
"returnonassets",
"returnoncapitalemployed",
"returnonequity",
"assetturnover",
"fixedassetturnover"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,642 |
363,551 | predictive_accuracy | accuracy_score | maternal_health_risk | Additional Information
Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, HeartRate and RiskLevel. All these are the responsible and significant risk factors for maternal mortality, that is one of the main concern of SDG of UN.
Has Missing Values?... | {0: [0 - Age (numeric)],
1: [1 - SystolicBP (numeric)],
2: [2 - DiastolicBP (numeric)],
3: [3 - BS (numeric)],
4: [4 - BodyTemp (numeric)],
5: [5 - HeartRate (numeric)],
6: [6 - RiskLevel (nominal)]} | {'MajorityClassSize': 406.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 272.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1014.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'... | maternal_health_risk | [
"Age",
"SystolicBP",
"DiastolicBP",
"BS",
"BodyTemp",
"HeartRate"
] | [
false,
false,
false,
false,
false,
false
] | 3,643 |
363,543 | predictive_accuracy | accuracy_score | QSAR_Bioconcentration_classification | the QSAR Bioconcentration Classes Dataset is a well-known dataset used in cheminformatics and environmental chemistry. It is available from the UCI Machine Learning Repository and is often used for classification and regression tasks related to predicting the bioconcentration factor (BCF) of chemical compounds.
Datase... | {0: [0 - CAS (string)],
1: [1 - SMILES (string)],
2: [2 - Set (string)],
3: [3 - nHM (numeric)],
4: [4 - piPC09 (numeric)],
5: [5 - PCD (numeric)],
6: [6 - X2Av (numeric)],
7: [7 - MLOGP (numeric)],
8: [8 - ON1V (numeric)],
9: [9 - N-072 (numeric)],
10: [10 - B02[C-N] (numeric)],
11: [11 - F04[C-O] (numeric)... | {'MajorityClassSize': 460.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 64.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 779.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | QSAR_Bioconcentration_classification | [
"CAS",
"SMILES",
"Set",
"nHM",
"piPC09",
"PCD",
"X2Av",
"MLOGP",
"ON1V",
"N-072",
"B02[C-N]",
"F04[C-O]"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,644 |
363,288 | predictive_accuracy | accuracy_score | Corporate_Credit | # Credit Ratings of Big US Firms and their Financials | {0: [0 - name (string)],
1: [1 - symbol (string)],
2: [2 - rating_agency_name (string)],
3: [3 - date (string)],
4: [4 - sector (string)],
5: [5 - currentratio (numeric)],
6: [6 - quickratio (numeric)],
7: [7 - cashratio (numeric)],
8: [8 - daysofsalesoutstanding (numeric)],
9: [9 - netprofitmargin (numeric)],... | {'MajorityClassSize': 671.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2029.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 25.0,
'NumberOfSymbolicFeatures': 1.0,
... | Corporate_Credit | [
"name",
"symbol",
"rating_agency_name",
"date",
"sector",
"currentratio",
"quickratio",
"cashratio",
"daysofsalesoutstanding",
"netprofitmargin",
"pretaxprofitmargin",
"grossprofitmargin",
"operatingprofitmargin",
"returnonassets",
"returnoncapitalemployed",
"returnonequity",
"assett... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,646 |
363,282 | predictive_accuracy | accuracy_score | dataset_credit-approval | Financial dataset for automl benchmark. Name = dataset_credit-approval, target = class | {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 - a11 (numeric)],
11: [11 - a12 (numeric)],
12: [12 - a13 (numeric)]... | {'MajorityClassSize': 383.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 307.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 690.0,
'NumberOfInstancesWithMissingValues': 24.0,
'NumberOfMissingValues': 25.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 0.0,... | dataset_credit-approval | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"a7",
"a8",
"a9",
"a10",
"a11",
"a12",
"a13",
"a14",
"a15"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,647 |
363,560 | predictive_accuracy | accuracy_score | bone_marrow_transplant_children | Dataset Information
Additional Information
The data set describes pediatric patients with several hematologic diseases: malignant disorders (i.a. acute lymphoblastic leukemia, acute myelogenous leukemia, chronic myelogenous leukemia, myelodysplastic syndrome) and nonmalignant cases (i.a. severe aplastic anemia, Fancon... | {0: [0 - Recipientgender (numeric)],
1: [1 - Stemcellsource (numeric)],
2: [2 - Donorage (numeric)],
3: [3 - Donorage35 (numeric)],
4: [4 - IIIV (numeric)],
5: [5 - Gendermatch (numeric)],
6: [6 - DonorABO (numeric)],
7: [7 - RecipientABO (numeric)],
8: [8 - RecipientRh (numeric)],
9: [9 - ABOmatch (numeric)],... | {'MajorityClassSize': 102.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 85.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 187.0,
'NumberOfInstancesWithMissingValues': 45.0,
'NumberOfMissingValues': 81.0,
'NumberOfNumericFeatures': 35.0,
'NumberOfSymbolicFeatures': 1.0,
... | bone_marrow_transplant_children | [
"Recipientgender",
"Stemcellsource",
"Donorage",
"Donorage35",
"IIIV",
"Gendermatch",
"DonorABO",
"RecipientABO",
"RecipientRh",
"ABOmatch",
"CMVstatus",
"DonorCMV",
"RecipientCMV",
"Disease",
"Riskgroup",
"Txpostrelapse",
"Diseasegroup",
"HLAmatch",
"HLAmismatch",
"Antigen",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,648 |
363,497 | predictive_accuracy | accuracy_score | mice | Protein expression levels of 77 proteins measured in the nuclear fraction of cortex from control and Down syndrome mice (Ts65Dn).
First column indicates mouse id.
For each mouse 15 measurements were registered.
Last four columns indicate: Genotype (control or trisomy), Treatment (memantine or saline), Behavior (contex... | {0: [0 - MouseID (string)],
1: [1 - DYRK1A_N (numeric)],
2: [2 - ITSN1_N (numeric)],
3: [3 - BDNF_N (numeric)],
4: [4 - NR1_N (numeric)],
5: [5 - NR2A_N (numeric)],
6: [6 - pAKT_N (numeric)],
7: [7 - pBRAF_N (numeric)],
8: [8 - pCAMKII_N (numeric)],
9: [9 - pCREB_N (numeric)],
10: [10 - pELK_N (numeric)],
11... | {'MajorityClassSize': 150.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 105.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 82.0,
'NumberOfInstances': 1095.0,
'NumberOfInstancesWithMissingValues': 543.0,
'NumberOfMissingValues': 1906.0,
'NumberOfNumericFeatures': 77.0,
'NumberOfSymbolicFeatures': ... | mice | [
"MouseID",
"DYRK1A_N",
"ITSN1_N",
"BDNF_N",
"NR1_N",
"NR2A_N",
"pAKT_N",
"pBRAF_N",
"pCAMKII_N",
"pCREB_N",
"pELK_N",
"pERK_N",
"pJNK_N",
"PKCA_N",
"pMEK_N",
"pNR1_N",
"pNR2A_N",
"pNR2B_N",
"pPKCAB_N",
"pRSK_N",
"AKT_N",
"BRAF_N",
"CAMKII_N",
"CREB_N",
"ELK_N",
"ERK... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,649 |
363,406 | predictive_accuracy | accuracy_score | Cirrhosis_Patient_Survival_Prediction | For what purpose was the dataset created?
Cirrhosis results from prolonged liver damage, leading to extensive scarring, often due to conditions like hepatitis or chronic alcohol consumption. The data provided is sourced from a Mayo Clinic study on primary biliary cirrhosis (PBC) of the liver carried out from 1974 to 1... | {0: [0 - Drug (string)],
1: [1 - Age (numeric)],
2: [2 - Sex (string)],
3: [3 - Ascites (string)],
4: [4 - Hepatomegaly (string)],
5: [5 - Spiders (string)],
6: [6 - Edema (string)],
7: [7 - Bilirubin (numeric)],
8: [8 - Cholesterol (string)],
9: [9 - Albumin (numeric)],
10: [10 - Copper (string)],
11: [11 -... | {'MajorityClassSize': 232.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 25.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 18.0,
'NumberOfInstances': 418.0,
'NumberOfInstancesWithMissingValues': 106.0,
'NumberOfMissingValues': 965.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,... | Cirrhosis_Patient_Survival_Prediction | [
"Drug",
"Age",
"Sex",
"Ascites",
"Hepatomegaly",
"Spiders",
"Edema",
"Bilirubin",
"Cholesterol",
"Albumin",
"Copper",
"Alk_Phos",
"SGOT",
"Tryglicerides",
"Platelets",
"Prothrombin",
"Stage"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,650 |
363,407 | predictive_accuracy | accuracy_score | Predicting_Risk_Factors_of_Chronic_Kidney_Disease | Was there any data preprocessing performed?
This dataset is not pre-processed, if you want to apply a Machine learning Algorithm at first you have to need to pre-process the data
Additional Information
This dataset is real Bangladeshi patient data. The dataset is collected from Enam Medical College, Savar, Dhaka, Ba... | {0: [0 - bp (Diastolic) (numeric)],
1: [1 - bp limit (numeric)],
2: [2 - sg (string)],
3: [3 - al (string)],
4: [4 - rbc (numeric)],
5: [5 - su (string)],
6: [6 - pc (numeric)],
7: [7 - pcc (numeric)],
8: [8 - ba (numeric)],
9: [9 - bgr (string)],
10: [10 - bu (string)],
11: [11 - sod (string)],
12: [12 - s... | {'MajorityClassSize': 128.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 72.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 29.0,
'NumberOfInstances': 200.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 0.0,
'... | Predicting_Risk_Factors_of_Chronic_Kidney_Disease | [
"bp (Diastolic)",
"bp limit",
"sg",
"al",
"rbc",
"su",
"pc",
"pcc",
"ba",
"bgr",
"bu",
"sod",
"sc",
"pot",
"hemo",
"pcv",
"rbcc",
"wbcc",
"htn",
"dm",
"cad",
"appet",
"pe",
"ane",
"grf",
"stage",
"affected",
"age"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,653 |
363,585 | predictive_accuracy | accuracy_score | divorce_prediction | Divorce Prediction
About Dataset
Abstract
Ever been heart broken and/or wondered what makes a lasting relationship? This dataset may help you.
About this dataset
This dataset contains data about 150 couples with their corresponding Divorce Predictors Scale variables (DPS) on the basis of Gottman couples therapy.
The ... | {0: [0 - Q1 (numeric)],
1: [1 - Q2 (numeric)],
2: [2 - Q3 (numeric)],
3: [3 - Q4 (numeric)],
4: [4 - Q5 (numeric)],
5: [5 - Q6 (numeric)],
6: [6 - Q7 (numeric)],
7: [7 - Q8 (numeric)],
8: [8 - Q9 (numeric)],
9: [9 - Q10 (numeric)],
10: [10 - Q11 (numeric)],
11: [11 - Q12 (numeric)],
12: [12 - Q13 (numeric)]... | {'MajorityClassSize': 86.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 84.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 55.0,
'NumberOfInstances': 170.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 54.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | divorce_prediction | [
"Q1",
"Q2",
"Q3",
"Q4",
"Q5",
"Q6",
"Q7",
"Q8",
"Q9",
"Q10",
"Q11",
"Q12",
"Q13",
"Q14",
"Q15",
"Q16",
"Q17",
"Q18",
"Q19",
"Q20",
"Q21",
"Q22",
"Q23",
"Q24",
"Q25",
"Q26",
"Q27",
"Q28",
"Q29",
"Q30",
"Q31",
"Q32",
"Q33",
"Q34",
"Q35",
"Q36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,654 |
363,293 | predictive_accuracy | accuracy_score | Apple_Stock_Price_Trends_Classification | # Apple Stock Price Trend Prediction Dataset (2014-2023)
## Dataset Overview
Apple stock prices from years 2014 to 2023. This dataset can be used to predict price trend for next day based on technical indicators. | {0: [0 - open (numeric)],
1: [1 - high (numeric)],
2: [2 - low (numeric)],
3: [3 - close (numeric)],
4: [4 - volume (numeric)],
5: [5 - rsi_7 (numeric)],
6: [6 - rsi_14 (numeric)],
7: [7 - cci_7 (numeric)],
8: [8 - cci_14 (numeric)],
9: [9 - sma_50 (numeric)],
10: [10 - ema_50 (numeric)],
11: [11 - sma_100 (... | {'MajorityClassSize': 951.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 779.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 2516.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22.0,
'NumberOfSymbolicFeatures': 1.0,
... | Apple_Stock_Price_Trends_Classification | [
"open",
"high",
"low",
"close",
"volume",
"rsi_7",
"rsi_14",
"cci_7",
"cci_14",
"sma_50",
"ema_50",
"sma_100",
"ema_100",
"macd",
"bollinger",
"truerange",
"atr_7",
"atr_14",
"year",
"month",
"day",
"weekday"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,655 |
363,666 | predictive_accuracy | accuracy_score | students_dropout_and_academic_success | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - Marital_status (nominal)],
1: [1 - Application_mode (nominal)],
2: [2 - Application_order (numeric)],
3: [3 - Course (nominal)],
4: [4 - Daytimeevening_attendance (nominal)],
5: [5 - Previous_qualification (nominal)],
6: [6 - Previous_qualification_grade (numeric)],
7: [7 - Nationality (nominal)],
8: [... | {'MajorityClassSize': 2209.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 794.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 4424.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 18.0... | students_dropout_and_academic_success | [
"Marital_status",
"Application_mode",
"Application_order",
"Course",
"Daytimeevening_attendance",
"Previous_qualification",
"Previous_qualification_grade",
"Nationality",
"Mothers_qualification",
"Fathers_qualification",
"Mothers_occupation",
"Fathers_occupation",
"Admission_grade",
"Displ... | [
true,
true,
false,
true,
true,
true,
false,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,656 |
363,441 | predictive_accuracy | accuracy_score | diabetes_risk_prediction_dataset | Dataset Description: Early Stage Diabetes Risk Prediction
This dataset comprises crucial sign and symptom data of individuals who either exhibit early signs of diabetes or are at risk of developing diabetes. The variables included in the dataset provide valuable insights into potential indicators of diabetes onset. Th... | {0: [0 - Age (numeric)],
1: [1 - Gender (string)],
2: [2 - Polyuria (string)],
3: [3 - Polydipsia (string)],
4: [4 - sudden weight loss (string)],
5: [5 - weakness (string)],
6: [6 - Polyphagia (string)],
7: [7 - Genital thrush (string)],
8: [8 - visual blurring (string)],
9: [9 - Itching (string)],
10: [10 -... | {'MajorityClassSize': 320.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 520.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
'... | diabetes_risk_prediction_dataset | [
"Age",
"Gender",
"Polyuria",
"Polydipsia",
"sudden weight loss",
"weakness",
"Polyphagia",
"Genital thrush",
"visual blurring",
"Itching",
"Irritability",
"delayed healing",
"partial paresis",
"muscle stiffness",
"Alopecia",
"Obesity"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,657 |
363,456 | predictive_accuracy | accuracy_score | PostPartum_Depression | PostPartum Depression
Mental Disorder of pregnant women
About Dataset
In our research, we gathered a dataset of 1503 records from a medical hospital using a
questionnaire administered through a Google form. This dataset has not yet been published.
Our dataset includes 15 attributes, where I select 10 attributes, 9 of... | {0: [0 - Timestamp (string)],
1: [1 - Age (string)],
2: [2 - Feeling sad or Tearful (string)],
3: [3 - Irritable towards baby & partner (string)],
4: [4 - Trouble sleeping at night (string)],
5: [5 - Problems concentrating or making decision (string)],
6: [6 - Overeating or loss of appetite (string)],
7: [7 - Fe... | {'MajorityClassSize': 980.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 523.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 1503.0,
'NumberOfInstancesWithMissingValues': 12.0,
'NumberOfMissingValues': 27.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,... | PostPartum_Depression | [
"Timestamp",
"Age",
"Feeling sad or Tearful",
"Irritable towards baby & partner",
"Trouble sleeping at night",
"Problems concentrating or making decision",
"Overeating or loss of appetite",
"Feeling of guilt",
"Problems of bonding with baby",
"Suicide attempt"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,658 |
363,645 | predictive_accuracy | accuracy_score | Is-this-a-good-customer | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - month (numeric)],
1: [1 - credit_amount (numeric)],
2: [2 - credit_term (numeric)],
3: [3 - age (numeric)],
4: [4 - sex (nominal)],
5: [5 - education (nominal)],
6: [6 - product_type (nominal)],
7: [7 - having_children_flg (nominal)],
8: [8 - region (nominal)],
9: [9 - income (numeric)],
10: [10 - fa... | {'MajorityClassSize': 1527.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 196.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 1723.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 9.0,
... | Is-this-a-good-customer | [
"month",
"credit_amount",
"credit_term",
"age",
"sex",
"education",
"product_type",
"having_children_flg",
"region",
"income",
"family_status",
"phone_operator",
"is_client"
] | [
false,
false,
false,
false,
true,
true,
true,
true,
true,
false,
true,
true,
true
] | 3,661 |
363,294 | predictive_accuracy | accuracy_score | Corporate_Credit_Rating | # Credit Ratings of Big US Firms
## Context
A corporate credit rating expresses the ability of a firm to repay its debt to creditors. Credit rating agencies assess companies' creditworthiness based on financial indicators. | {0: [0 - rating (nominal)],
1: [1 - currentratio (numeric)],
2: [2 - quickratio (numeric)],
3: [3 - cashratio (numeric)],
4: [4 - daysofsalesoutstanding (numeric)],
5: [5 - netprofitmargin (numeric)],
6: [6 - pretaxprofitmargin (numeric)],
7: [7 - grossprofitmargin (numeric)],
8: [8 - operatingprofitmargin (num... | {'MajorityClassSize': 671.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 46.0,
'NumberOfInstances': 2029.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 28.0,
'NumberOfSymbolicFeatures': 18.0,
... | Corporate_Credit_Rating | [
"currentratio",
"quickratio",
"cashratio",
"daysofsalesoutstanding",
"netprofitmargin",
"pretaxprofitmargin",
"grossprofitmargin",
"operatingprofitmargin",
"returnonassets",
"returnoncapitalemployed",
"returnonequity",
"assetturnover",
"fixedassetturnover",
"debtequityratio",
"debtratio"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
... | 3,662 |
363,582 | predictive_accuracy | accuracy_score | student_lifestyle_dataset | Daily Lifestyle and Academic Performance of Students
About Dataset
This dataset provides a detailed view of student lifestyle patterns and their correlation with academic performance, represented by GPA. It contains 2,000 records of students' daily habits across study, extracurriculars, sleep, socializing, and physica... | {0: [0 - Study_Hours_Per_Day (numeric)],
1: [1 - Extracurricular_Hours_Per_Day (numeric)],
2: [2 - Sleep_Hours_Per_Day (numeric)],
3: [3 - Social_Hours_Per_Day (numeric)],
4: [4 - Physical_Activity_Hours_Per_Day (numeric)],
5: [5 - GPA (numeric)],
6: [6 - Stress_Level (nominal)]} | {'MajorityClassSize': 1029.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 297.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | student_lifestyle_dataset | [
"Study_Hours_Per_Day",
"Extracurricular_Hours_Per_Day",
"Sleep_Hours_Per_Day",
"Social_Hours_Per_Day",
"Physical_Activity_Hours_Per_Day",
"GPA"
] | [
false,
false,
false,
false,
false,
false
] | 3,663 |
363,564 | predictive_accuracy | accuracy_score | hepatitis_c_virus_hcv_for_egyptian_patients | Egyptian patients who underwent treatment dosages for HCV about 18 months. Discretization should be applied based on expert recommendations; there is an attached file shows how.
Age Age
Gender Gender
BMI Body Mass Index
Fever Fever
Nausea/Vomting Nausea/Vomting
Headache Headache
Diarrhea Diarrhea
Fatigue & generalized... | {0: [0 - Age_ (numeric)],
1: [1 - Gender (numeric)],
2: [2 - BMI (numeric)],
3: [3 - Fever (numeric)],
4: [4 - Nausea/Vomting (numeric)],
5: [5 - Headache_ (numeric)],
6: [6 - Diarrhea_ (numeric)],
7: [7 - Fatigue_&_generalized_bone_ache_ (numeric)],
8: [8 - Jaundice_ (numeric)],
9: [9 - Epigastric_pain_ (nume... | {'MajorityClassSize': 362.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 332.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 29.0,
'NumberOfInstances': 1385.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 28.0,
'NumberOfSymbolicFeatures': 1.0,
... | hepatitis_c_virus_hcv_for_egyptian_patients | [
"Age_",
"Gender",
"BMI",
"Fever",
"Nausea/Vomting",
"Headache_",
"Diarrhea_",
"Fatigue_&_generalized_bone_ache_",
"Jaundice_",
"Epigastric_pain_",
"WBC",
"RBC",
"HGB",
"Plat",
"AST_1",
"ALT_1",
"ALT4",
"ALT_12",
"ALT_24",
"ALT_36",
"ALT_48",
"ALT_after_24_w",
"RNA_Base",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,664 |
363,427 | predictive_accuracy | accuracy_score | mental-health-in-tech-survey | About Dataset
Dataset Information
This dataset is from a 2014 survey that measures attitudes towards mental health and frequency of mental health disorders in the tech workplace. You are also encouraged to analyze data from the ongoing 2016 survey found here.
Content
This dataset contains the following data:
Timestam... | {0: [0 - Timestamp (string)],
1: [1 - Age (numeric)],
2: [2 - Gender (string)],
3: [3 - Country (string)],
4: [4 - state (string)],
5: [5 - self_employed (string)],
6: [6 - family_history (string)],
7: [7 - treatment (string)],
8: [8 - work_interfere (string)],
9: [9 - no_employees (string)],
10: [10 - remote... | {'MajorityClassSize': 1031.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 228.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 27.0,
'NumberOfInstances': 1259.0,
'NumberOfInstancesWithMissingValues': 1173.0,
'NumberOfMissingValues': 1892.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures':... | mental-health-in-tech-survey | [
"Timestamp",
"Age",
"Gender",
"Country",
"state",
"self_employed",
"family_history",
"treatment",
"work_interfere",
"no_employees",
"remote_work",
"benefits",
"care_options",
"wellness_program",
"seek_help",
"anonymity",
"leave",
"mental_health_consequence",
"phys_health_conseque... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,665 |
363,586 | predictive_accuracy | accuracy_score | depression_2020 | Context
Hi,
The original Dataset wad published by Frankcc in the following link: Link Kaggle
The dataset is involved into the analysis of depression. The data was consists as a study about the life conditions of people who live in rurales zones. Because all the columns were not explicated in this challenge so We can'... | {0: [0 - sex (numeric)],
1: [1 - Age (numeric)],
2: [2 - Married (numeric)],
3: [3 - Number_children (numeric)],
4: [4 - education_level (numeric)],
5: [5 - total_members (numeric)],
6: [6 - gained_asset (numeric)],
7: [7 - durable_asset (numeric)],
8: [8 - save_asset (numeric)],
9: [9 - living_expenses (numer... | {'MajorityClassSize': 1191.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 238.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 1429.0,
'NumberOfInstancesWithMissingValues': 20.0,
'NumberOfMissingValues': 20.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 1.... | depression_2020 | [
"sex",
"Age",
"Married",
"Number_children",
"education_level",
"total_members",
"gained_asset",
"durable_asset",
"save_asset",
"living_expenses",
"other_expenses",
"incoming_salary",
"incoming_own_farm",
"incoming_business",
"incoming_no_business",
"incoming_agricultural",
"farm_expe... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,666 |
363,403 | predictive_accuracy | accuracy_score | Estimation_of_Obesity_Levels | This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records, the records are labeled with the class variable NObesity (Obesity Level), that allows clas... | {0: [0 - Gender (string)],
1: [1 - Age (numeric)],
2: [2 - Height (numeric)],
3: [3 - Weight (numeric)],
4: [4 - family_history_with_overweight (string)],
5: [5 - FAVC (string)],
6: [6 - FCVC (numeric)],
7: [7 - NCP (numeric)],
8: [8 - CAEC (string)],
9: [9 - SMOKE (string)],
10: [10 - CH2O (numeric)],
11: [... | {'MajorityClassSize': 351.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 272.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 2111.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
... | Estimation_of_Obesity_Levels | [
"Gender",
"Age",
"Height",
"Weight",
"family_history_with_overweight",
"FAVC",
"FCVC",
"NCP",
"CAEC",
"SMOKE",
"CH2O",
"SCC",
"FAF",
"TUE",
"CALC",
"MTRANS"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,667 |
363,547 | predictive_accuracy | accuracy_score | Cervical_Cancer_Risk_Factors | Cervical cancer (Risk Factors) Data Set (multilabel classification)
The dataset was collected at 'Hospital Universitario de Caracas' in Caracas, Venezuela. The dataset comprises demographic information, habits, and historic medical records of 858 patients. Several patients decided not to answer some of the questions b... | {0: [0 - Age (numeric)],
1: [1 - Number of sexual partners (numeric)],
2: [2 - First sexual intercourse (numeric)],
3: [3 - Num of pregnancies (numeric)],
4: [4 - Smokes (numeric)],
5: [5 - Smokes (years) (numeric)],
6: [6 - Smokes (packs/year) (numeric)],
7: [7 - Hormonal Contraceptives (numeric)],
8: [8 - Hor... | {'MajorityClassSize': 756.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 10.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 858.0,
'NumberOfInstancesWithMissingValues': 799.0,
'NumberOfMissingValues': 3622.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.... | Cervical_Cancer_Risk_Factors | [
"Age",
"Number of sexual partners",
"First sexual intercourse",
"Num of pregnancies",
"Smokes",
"Smokes (years)",
"Smokes (packs/year)",
"Hormonal Contraceptives",
"Hormonal Contraceptives (years)",
"IUD",
"IUD (years)",
"STDs",
"STDs (number)",
"STDs:condylomatosis",
"STDs:cervical cond... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,668 |
363,554 | predictive_accuracy | accuracy_score | regensburg_pediatric_appendicitis | This dataset was acquired in a retrospective study from a cohort of pediatric patients admitted with abdominal pain to Children's Hospital St. Hedwig in Regensburg, Germany. Multiple abdominal B-mode ultrasound images were acquired for most patients, with the number of views varying from 1 to 15. The images depict vari... | {0: [0 - Age (numeric)],
1: [1 - BMI (numeric)],
2: [2 - Sex (string)],
3: [3 - Height (numeric)],
4: [4 - Weight (numeric)],
5: [5 - Length_of_Stay (numeric)],
6: [6 - Alvarado_Score (numeric)],
7: [7 - Paedriatic_Appendicitis_Score (numeric)],
8: [8 - Appendix_on_US (string)],
9: [9 - Appendix_Diameter (nume... | {'MajorityClassSize': 483.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 56.0,
'NumberOfInstances': 781.0,
'NumberOfInstancesWithMissingValues': 781.0,
'NumberOfMissingValues': 13928.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 1.... | regensburg_pediatric_appendicitis | [
"Age",
"BMI",
"Sex",
"Height",
"Weight",
"Length_of_Stay",
"Alvarado_Score",
"Paedriatic_Appendicitis_Score",
"Appendix_on_US",
"Appendix_Diameter",
"Migratory_Pain",
"Lower_Right_Abd_Pain",
"Contralateral_Rebound_Tenderness",
"Coughing_Pain",
"Nausea",
"Loss_of_Appetite",
"Body_Temp... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,669 |
363,574 | predictive_accuracy | accuracy_score | mental-health-in-tech-survey | About Dataset
Dataset Information
This dataset is from a 2014 survey that measures attitudes towards mental health and frequency of mental health disorders in the tech workplace. You are also encouraged to analyze data from the ongoing 2016 survey found here.
Content
This dataset contains the following data:
Timestam... | {0: [0 - Age (numeric)],
1: [1 - Gender (string)],
2: [2 - Country (string)],
3: [3 - state (string)],
4: [4 - self_employed (string)],
5: [5 - family_history (string)],
6: [6 - treatment (string)],
7: [7 - work_interfere (string)],
8: [8 - no_employees (string)],
9: [9 - remote_work (string)],
10: [10 - tech... | {'MajorityClassSize': 1031.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 228.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 26.0,
'NumberOfInstances': 1259.0,
'NumberOfInstancesWithMissingValues': 1173.0,
'NumberOfMissingValues': 1892.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures':... | mental-health-in-tech-survey | [
"Age",
"Gender",
"Country",
"state",
"self_employed",
"family_history",
"treatment",
"work_interfere",
"no_employees",
"remote_work",
"benefits",
"care_options",
"wellness_program",
"seek_help",
"anonymity",
"leave",
"mental_health_consequence",
"phys_health_consequence",
"cowork... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,670 |
363,656 | predictive_accuracy | accuracy_score | polish_companies_bankruptcy | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - net_profit_to_total_assets (numeric)],
1: [1 - total_liabilities_to_total_assets (numeric)],
2: [2 - working_capital_to_total_assets (numeric)],
3: [3 - current_assets_to_short_term_liabilities (numeric)],
4: [4 - liquidity_days_ratio (numeric)],
5: [5 - retained_earnings_to_total_assets (numeric)],
6: [... | {'MajorityClassSize': 5500.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 410.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 65.0,
'NumberOfInstances': 5910.0,
'NumberOfInstancesWithMissingValues': 2879.0,
'NumberOfMissingValues': 4666.0,
'NumberOfNumericFeatures': 64.0,
'NumberOfSymbolicFeatures'... | polish_companies_bankruptcy | [
"net_profit_to_total_assets",
"total_liabilities_to_total_assets",
"working_capital_to_total_assets",
"current_assets_to_short_term_liabilities",
"liquidity_days_ratio",
"retained_earnings_to_total_assets",
"ebit_to_total_assets",
"book_value_equity_to_total_liabilities",
"sales_to_total_assets",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,671 |
363,648 | predictive_accuracy | accuracy_score | maternal_health_risk | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - Age (numeric)],
1: [1 - SystolicBP (numeric)],
2: [2 - DiastolicBP (numeric)],
3: [3 - BS (numeric)],
4: [4 - BodyTemp (numeric)],
5: [5 - HeartRate (numeric)],
6: [6 - RiskLevel (nominal)]} | {'MajorityClassSize': 406.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 272.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1014.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'... | maternal_health_risk | [
"Age",
"SystolicBP",
"DiastolicBP",
"BS",
"BodyTemp",
"HeartRate"
] | [
false,
false,
false,
false,
false,
false
] | 3,672 |
363,799 | predictive_accuracy | accuracy_score | Pancreas-diabetes-1.1 | A binary histology image classification dataset consisting of 90 RGB microscopy images of pancreatic tissue acquired at 100x magnification with a resolution of 1280x960 pixels. The dataset includes 58 images of normal pancreatic tissue and 32 images of glucose-intolerant tissue. Intended for research in medical ima... | {0: [0 - filepath (string)], 1: [1 - class (string)]} | {'MajorityClassSize': 58.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 32.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1.0,
'NumberOfInstances': 90.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Pancreas-diabetes-1.1 | [] | [] | 3,673 |
361,868 | predictive_accuracy | accuracy_score | timing-attack-dataset-20-micro-seconds-delay-2022-09-19 | Bleichenbacher Timing Attack: 20 micro seconds dataset created on 2022-09-19
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination ... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 976.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 868.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 155.0,
'NumberOfInstances': 9996.0,
'NumberOfInstancesWithMissingValues': 9995.0,
'NumberOfMissingValues': 299850.0,
'NumberOfNumericFeatures': 154.0,
'NumberOfSymbolicFeatu... | timing-attack-dataset-20-micro-seconds-delay-2022-09-19 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,674 |
363,445 | predictive_accuracy | accuracy_score | agriculture_dataset_karnataka | Data Description
The dataset you've provided appears to capture agricultural data for Karnataka, specifically focusing on crop yields in Mangalore. Key features include the year of production, geographic details, and environmental conditions such as rainfall (measured in mm), temperature (in degrees Celsius), and humi... | {0: [0 - Year (numeric)],
1: [1 - Location (string)],
2: [2 - Area (numeric)],
3: [3 - Rainfall (numeric)],
4: [4 - Temperature (numeric)],
5: [5 - Soil type (string)],
6: [6 - Irrigation (string)],
7: [7 - yeilds (numeric)],
8: [8 - Humidity (numeric)],
9: [9 - Crops (string)],
10: [10 - price (numeric)],
1... | {'MajorityClassSize': 1458.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 21.0,
'NumberOfClasses': 13.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 3158.0,
'NumberOfInstancesWithMissingValues': 58.0,
'NumberOfMissingValues': 58.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0... | agriculture_dataset_karnataka | [
"Year",
"Location",
"Area",
"Rainfall",
"Temperature",
"Soil type",
"Irrigation",
"yeilds",
"Humidity",
"price",
"Season"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,675 |
363,070 | predictive_accuracy | accuracy_score | Lymphoma-3 | **Diffuse large b-cell lymphoma (Lymphoma 3 classes, DLBCL) dataset**
**Authors**: A. Alizadeh, M. Eisen, R. Davis, C. Ma, I. Lossos, A. Rosenwald, J. Boldrick, H. Sabet, T. Tran, X. Yu, et al
**Please cite**: ([URL](https://www.nature.com/articles/35000501)): A. Alizadeh, M. Eisen, R. Davis, C. Ma, I. Lossos, A. Ros... | {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': nan,
'MinorityClassSize': 9.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 4027.0,
'NumberOfInstances': 66.0,
'NumberOfInstancesWithMissingValues': 59.0,
'NumberOfMissingValues': 12269.0,
'NumberOfNumericFeatures': 4026.0,
'NumberOfSymbolicFeatures': 0... | Lymphoma-3 | [
"GENE1835X",
"GENE1836X",
"GENE1865X",
"GENE1380X",
"GENE1933X",
"GENE1932X",
"GENE1931X",
"GENE1930X",
"GENE3129X",
"GENE3126X",
"GENE0X",
"GENE3115X",
"GENE3116X",
"GENE3117X",
"GENE3118X",
"GENE3073X",
"GENE3072X",
"GENE3067X",
"GENE3068X",
"GENE3069X",
"GENE2584X",
"GEN... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,676 |
363,579 | predictive_accuracy | accuracy_score | agriculture_dataset_karnataka | Data Description
The dataset you've provided appears to capture agricultural data for Karnataka, specifically focusing on crop yields in Mangalore. Key features include the year of production, geographic details, and environmental conditions such as rainfall (measured in mm), temperature (in degrees Celsius), and humi... | {0: [0 - Year (numeric)],
1: [1 - Location (string)],
2: [2 - Area (numeric)],
3: [3 - Rainfall (numeric)],
4: [4 - Temperature (numeric)],
5: [5 - Soil type (string)],
6: [6 - Irrigation (string)],
7: [7 - yeilds (numeric)],
8: [8 - Humidity (numeric)],
9: [9 - Crops (nominal)],
10: [10 - price (numeric)],
... | {'MajorityClassSize': 1458.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 21.0,
'NumberOfClasses': 13.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 3158.0,
'NumberOfInstancesWithMissingValues': 58.0,
'NumberOfMissingValues': 58.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 1.0... | agriculture_dataset_karnataka | [
"Year",
"Location",
"Area",
"Rainfall",
"Temperature",
"Soil type",
"Irrigation",
"yeilds",
"Humidity",
"price",
"Season"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,677 |
363,450 | predictive_accuracy | accuracy_score | student_lifestyle_dataset | Daily Lifestyle and Academic Performance of Students
About Dataset
This dataset provides a detailed view of student lifestyle patterns and their correlation with academic performance, represented by GPA. It contains 2,000 records of students' daily habits across study, extracurriculars, sleep, socializing, and physica... | {0: [0 - Student_ID (numeric)],
1: [1 - Study_Hours_Per_Day (numeric)],
2: [2 - Extracurricular_Hours_Per_Day (numeric)],
3: [3 - Sleep_Hours_Per_Day (numeric)],
4: [4 - Social_Hours_Per_Day (numeric)],
5: [5 - Physical_Activity_Hours_Per_Day (numeric)],
6: [6 - GPA (numeric)],
7: [7 - Stress_Level (string)]} | {'MajorityClassSize': 1029.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 297.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
... | student_lifestyle_dataset | [
"Student_ID",
"Study_Hours_Per_Day",
"Extracurricular_Hours_Per_Day",
"Sleep_Hours_Per_Day",
"Social_Hours_Per_Day",
"Physical_Activity_Hours_Per_Day",
"GPA"
] | [
false,
false,
false,
false,
false,
false,
false
] | 3,678 |
363,669 | predictive_accuracy | accuracy_score | website_phishing | This dataset was curated for [TabArena](https://tabarena.ai/) by the TabArena team
as part of the [TabArena Tabular ML IID Study](https://tabarena.ai/data-tabular-ml-iid-study).
For more details on the study, see our [paper](https://tabarena.ai/paper-tabular-ml-iid-study).
**Dataset Focus**: This dataset shall be used... | {0: [0 - SFH (nominal)],
1: [1 - popUpWidnow (nominal)],
2: [2 - SSLfinal_State (nominal)],
3: [3 - Request_URL (nominal)],
4: [4 - URL_of_Anchor (nominal)],
5: [5 - web_traffic (nominal)],
6: [6 - URL_Length (nominal)],
7: [7 - age_of_domain (nominal)],
8: [8 - having_IP_Address (nominal)],
9: [9 - WebsiteTyp... | {'MajorityClassSize': 702.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 103.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1353.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 10.0,
... | website_phishing | [
"SFH",
"popUpWidnow",
"SSLfinal_State",
"Request_URL",
"URL_of_Anchor",
"web_traffic",
"URL_Length",
"age_of_domain",
"having_IP_Address"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,679 |
363,590 | predictive_accuracy | accuracy_score | air-quality-and-pollution-assessment | Environmental Metrics and Demographic Insights for Predicting Air Quality
About Dataset
This dataset focuses on air quality assessment across various regions. The dataset contains 5000 samples and captures critical environmental and demographic factors that influence pollution levels.
Key Features:
Temperature (C):... | {0: [0 - Temperature (numeric)],
1: [1 - Humidity (numeric)],
2: [2 - PM2.5 (numeric)],
3: [3 - PM10 (numeric)],
4: [4 - NO2 (numeric)],
5: [5 - SO2 (numeric)],
6: [6 - CO (numeric)],
7: [7 - Proximity_to_Industrial_Areas (numeric)],
8: [8 - Population_Density (numeric)],
9: [9 - Air_Quality (nominal)]} | {'MajorityClassSize': 2000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 500.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 5000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
... | air-quality-and-pollution-assessment | [
"Temperature",
"Humidity",
"PM2.5",
"PM10",
"NO2",
"SO2",
"CO",
"Proximity_to_Industrial_Areas",
"Population_Density"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,681 |
363,401 | predictive_accuracy | accuracy_score | HCV_data | Abstract of the paper (https://jlpm.amegroups.org/article/view/4401):
"
Background: Diagnostic pathways are based on expert rules ("if...then...else"), which can be visualized as decision trees. Machine learning algorithms may be used to validate existing or to suggest potential new decision trees.
Methods: We present... | {0: [0 - Age (numeric)],
1: [1 - Sex (string)],
2: [2 - ALB (numeric)],
3: [3 - ALP (numeric)],
4: [4 - AST (numeric)],
5: [5 - BIL (numeric)],
6: [6 - CHE (numeric)],
7: [7 - CHOL (numeric)],
8: [8 - CREA (numeric)],
9: [9 - CGT (numeric)],
10: [10 - PROT (numeric)],
11: [11 - ALT (numeric)],
12: [12 - Cat... | {'MajorityClassSize': 533.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 7.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 615.0,
'NumberOfInstancesWithMissingValues': 26.0,
'NumberOfMissingValues': 31.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
... | HCV_data | [
"Age",
"Sex",
"ALB",
"ALP",
"AST",
"BIL",
"CHE",
"CHOL",
"CREA",
"CGT",
"PROT",
"ALT"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,682 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.