Datasets:
time
int64 27
126
| Rad Flow
int64 -4,821
5.08k
| Fpv Close
int64 21
149
| Fpv Open
int64 -3,939
3.83k
| High
int64 -188
436
| Bypass
int64 -13,839
13.1k
| Bpv Close
int64 -48
105
| Bpv Open
int64 -353
270
| Unknown
int64 -356
266
| class
int64 1
7
|
|---|---|---|---|---|---|---|---|---|---|
50
| 21
| 77
| 0
| 28
| 0
| 27
| 48
| 22
| 2
|
55
| 0
| 92
| 0
| 0
| 26
| 36
| 92
| 56
| 4
|
53
| 0
| 82
| 0
| 52
| -5
| 29
| 30
| 2
| 1
|
37
| 0
| 76
| 0
| 28
| 18
| 40
| 48
| 8
| 1
|
37
| 0
| 79
| 0
| 34
| -26
| 43
| 46
| 2
| 1
|
85
| 0
| 88
| -4
| 6
| 1
| 3
| 83
| 80
| 5
|
56
| 0
| 81
| 0
| -4
| 11
| 25
| 86
| 62
| 4
|
55
| -1
| 95
| -3
| 54
| -4
| 40
| 41
| 2
| 1
|
53
| 8
| 77
| 0
| 28
| 0
| 23
| 48
| 24
| 4
|
37
| 0
| 101
| -7
| 28
| 0
| 64
| 73
| 8
| 1
|
37
| 0
| 78
| -2
| 12
| 0
| 42
| 65
| 24
| 1
|
45
| 0
| 84
| 0
| 46
| 20
| 38
| 37
| 0
| 1
|
38
| 2
| 77
| 0
| 38
| 7
| 39
| 38
| 0
| 1
|
37
| 0
| 78
| 0
| -2
| 5
| 41
| 81
| 40
| 1
|
41
| 0
| 100
| 0
| 38
| -8
| 59
| 61
| 2
| 1
|
41
| 0
| 89
| 1
| 38
| -16
| 48
| 50
| 2
| 1
|
47
| 0
| 85
| -2
| 46
| -4
| 38
| 39
| 0
| 1
|
49
| 0
| 79
| 0
| 46
| -5
| 30
| 32
| 2
| 1
|
49
| 3
| 82
| 0
| 50
| 4
| 33
| 33
| 0
| 1
|
37
| -318
| 105
| -2
| 36
| -4
| 68
| 69
| 0
| 1
|
44
| 3
| 77
| 0
| 44
| 2
| 33
| 33
| 0
| 1
|
106
| 2
| 108
| 0
| 70
| 0
| 1
| 38
| 36
| 5
|
37
| -5
| 76
| 0
| 28
| 0
| 40
| 48
| 8
| 1
|
49
| 0
| 100
| 0
| 50
| 19
| 52
| 51
| 0
| 1
|
45
| -1
| 87
| -2
| 44
| -4
| 43
| 43
| 0
| 1
|
37
| 4
| 80
| 0
| -2
| 0
| 43
| 83
| 40
| 1
|
55
| -1
| 98
| 0
| 52
| 8
| 42
| 46
| 4
| 4
|
55
| 0
| 86
| 8
| 54
| 0
| 31
| 32
| 0
| 1
|
55
| 3
| 88
| 3
| 54
| 0
| 32
| 33
| 2
| 1
|
37
| 0
| 74
| 0
| 26
| -1
| 38
| 48
| 10
| 1
|
37
| 0
| 106
| 0
| 36
| 3
| 68
| 69
| 2
| 1
|
49
| 0
| 107
| 0
| 46
| -16
| 58
| 60
| 2
| 1
|
79
| 5
| 83
| 0
| -46
| 0
| 4
| 130
| 126
| 5
|
51
| 0
| 79
| 0
| 52
| 29
| 28
| 27
| 0
| 1
|
59
| 4
| 84
| 0
| 60
| 24
| 25
| 24
| 0
| 1
|
37
| 0
| 108
| 0
| 30
| -21
| 71
| 77
| 6
| 1
|
56
| 3
| 97
| 0
| 46
| 9
| 41
| 51
| 10
| 4
|
39
| 5
| 77
| 0
| 38
| 0
| 37
| 38
| 0
| 1
|
51
| -5
| 81
| 0
| 52
| 0
| 30
| 30
| 0
| 1
|
49
| 0
| 77
| 0
| 46
| -9
| 29
| 31
| 2
| 1
|
46
| 2
| 77
| 0
| 44
| -19
| 32
| 34
| 2
| 1
|
56
| 0
| 81
| 0
| 56
| 0
| 24
| 24
| 0
| 1
|
56
| 0
| 79
| 0
| 16
| 3
| 23
| 63
| 40
| 4
|
37
| 0
| 105
| 0
| 34
| 4
| 68
| 71
| 4
| 1
|
76
| 0
| 81
| -2
| -40
| 13
| 5
| 122
| 118
| 5
|
56
| 0
| 80
| 0
| 56
| 7
| 24
| 23
| 0
| 1
|
41
| 2
| 76
| 0
| 42
| 11
| 35
| 35
| 0
| 1
|
41
| 1
| 85
| 0
| 38
| 0
| 44
| 46
| 2
| 1
|
56
| -4
| 97
| 0
| 52
| -2
| 41
| 45
| 4
| 4
|
49
| 0
| 95
| 1
| 50
| 2
| 46
| 46
| 0
| 1
|
37
| 2
| 76
| 0
| 36
| 0
| 38
| 39
| 2
| 1
|
56
| 1
| 99
| 0
| 50
| 16
| 42
| 49
| 8
| 4
|
56
| -4
| 88
| 0
| 56
| 0
| 31
| 31
| 0
| 1
|
37
| -3
| 81
| -2
| 38
| -9
| 43
| 42
| 0
| 1
|
47
| 0
| 84
| 4
| 46
| 0
| 38
| 38
| 0
| 1
|
41
| 1
| 86
| 2
| 42
| 14
| 45
| 45
| 0
| 1
|
53
| 3
| 81
| 0
| 54
| 0
| 27
| 26
| 0
| 1
|
47
| 5
| 79
| 0
| 46
| 0
| 32
| 32
| 0
| 1
|
104
| 7
| 105
| 7
| 70
| 0
| 1
| 35
| 34
| 5
|
101
| -3
| 102
| -4
| 72
| 0
| 1
| 29
| 28
| 5
|
45
| 0
| 86
| 0
| 44
| -17
| 41
| 42
| 2
| 1
|
42
| 4
| 77
| 0
| 42
| 1
| 35
| 35
| 0
| 1
|
45
| 0
| 79
| 0
| 44
| -4
| 34
| 35
| 2
| 1
|
37
| 0
| 80
| 0
| 36
| 15
| 43
| 44
| 2
| 1
|
56
| 0
| 96
| -1
| 44
| 0
| 40
| 52
| 12
| 4
|
56
| 0
| 81
| 0
| -10
| -3
| 25
| 92
| 66
| 4
|
42
| 0
| 83
| 2
| 42
| 0
| 41
| 42
| 2
| 1
|
51
| 1
| 88
| 0
| 52
| 7
| 37
| 37
| 0
| 1
|
49
| 3
| 82
| 0
| 46
| -8
| 33
| 35
| 2
| 1
|
41
| 1
| 86
| 3
| 42
| 6
| 45
| 45
| 0
| 1
|
37
| 0
| 77
| 0
| 336
| 4,910
| 41
| -258
| -298
| 1
|
49
| 0
| 84
| 0
| 46
| -4
| 36
| 38
| 2
| 1
|
37
| -1
| 77
| -3
| 34
| -6
| 40
| 43
| 2
| 1
|
41
| 0
| 79
| 0
| 38
| -23
| 38
| 41
| 2
| 1
|
51
| 0
| 79
| 0
| 52
| 2
| 27
| 27
| 0
| 1
|
55
| -1
| 79
| -4
| 20
| -12
| 24
| 59
| 34
| 4
|
41
| 0
| 77
| 0
| 42
| 5
| 37
| 36
| 0
| 1
|
46
| -3
| 80
| 0
| 46
| 0
| 34
| 34
| 0
| 1
|
56
| 0
| 79
| 0
| -2
| -24
| 24
| 82
| 58
| 4
|
44
| 0
| 82
| -1
| 44
| 0
| 38
| 38
| 0
| 1
|
45
| 0
| 83
| 0
| 46
| 31
| 37
| 36
| 0
| 1
|
37
| 0
| 77
| -1
| 36
| 4
| 40
| 41
| 0
| 1
|
37
| 0
| 75
| -1
| 36
| 0
| 38
| 39
| 0
| 1
|
37
| 1
| 77
| -2
| 34
| 0
| 40
| 43
| 2
| 1
|
51
| 0
| 79
| 0
| 50
| -6
| 28
| 30
| 2
| 1
|
46
| -4
| 76
| -1
| 46
| 0
| 29
| 29
| 0
| 1
|
49
| 0
| 77
| 5
| 50
| 5
| 28
| 28
| 0
| 1
|
45
| 0
| 108
| 0
| 44
| -3
| 62
| 64
| 2
| 1
|
42
| 0
| 79
| -2
| 42
| 0
| 36
| 37
| 2
| 1
|
37
| 0
| 78
| 0
| 20
| -10
| 41
| 57
| 16
| 1
|
37
| 0
| 76
| -3
| 36
| -10
| 39
| 40
| 2
| 1
|
53
| 5
| 84
| 0
| 52
| -5
| 31
| 32
| 2
| 1
|
38
| 4
| 76
| 0
| 38
| 4
| 38
| 37
| 0
| 1
|
40
| 0
| 80
| 0
| 38
| -2
| 40
| 41
| 2
| 1
|
47
| 2
| 95
| 3
| 46
| 0
| 48
| 49
| 0
| 1
|
45
| 0
| 79
| 5
| 44
| 0
| 34
| 35
| 2
| 1
|
41
| -1
| 86
| 0
| 42
| 0
| 45
| 45
| 0
| 1
|
55
| 0
| 95
| 0
| 36
| 13
| 40
| 59
| 20
| 4
|
56
| 0
| 95
| 0
| 56
| 15
| 40
| 39
| 0
| 1
|
51
| -3
| 83
| 0
| 50
| -14
| 32
| 33
| 2
| 1
|
UCI Tabular Benchmark Sample
This repository contains a small collection of tabular datasets mirrored from the UCI Machine Learning Repository and prepared for convenient experimentation.
Contents
The datasets are organized by common ML task type:
Binary Classification
Adult dataset
Bank Marketing dataset
Multiclassification
Covertype dataset
Statlog (Shuttle) dataset
Regression
Year Prediction MSD dataset
Source and License
All datasets in this repository are sourced from the UCI Machine Learning Repository and are used under Creative Commons Attribution 4.0 International (CC BY 4.0).
- CC BY 4.0 license text: https://creativecommons.org/licenses/by/4.0/
Upstream dataset pages (UCI)
- Adult (Census Income): https://archive.ics.uci.edu/dataset/2/adult
- Bank Marketing: https://archive.ics.uci.edu/dataset/222/bank+marketing
- Covertype: https://archive.ics.uci.edu/dataset/31/covertype
- Statlog (Shuttle): https://archive.ics.uci.edu/dataset/148/statlog+shuttle
- YearPredictionMSD: https://archive.ics.uci.edu/dataset/203/yearpredictionmsd
Modifications and Data Processing Notes
Some modifications may have been applied for usability and consistency. These modifications are intended to be non-substantive and not to change the meaning of the data.
Typical changes include:
- Adding column headers where the original files did not include headers.
- Converting original formats (for example space-separated or other delimiters) into
.csv. - Normalizing line endings and basic formatting fixes to improve parsing.
- In some cases, reorganizing files into a standard folder structure (for example
train.csvandtest.csv).
Unless explicitly stated in a dataset folder README (if present), no attempt was made to:
- Remove rows or features
- Alter feature values
- Rebalance classes
- Impute missing values
If you require a byte-for-byte identical copy of the upstream distribution, please download directly from the corresponding UCI page.
Missing Values
Missing values are preserved as in the upstream sources. Depending on the dataset, missingness may appear as empty fields, ?, or other dataset-specific markers. Refer to each dataset's UCI documentation for the authoritative description.
Intended Use
This pack is intended for:
- Tabular model benchmarking (linear models, tree models, neural networks)
- Privacy and security research on tabular learning pipelines, including:
- reconstruction and gradient inversion attacks
- defenses such as clipping, noise injection, discretization, constraint-aware decoding
- evaluating reconstructibility using feature-level and record-level metrics
It is not intended for making decisions about individuals or for any high-stakes deployment
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