Datasets:
File size: 3,060 Bytes
5ff9c05 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
license: cc-by-4.0
language:
- en
pretty_name: UCI Tabular Benchmark Sample
task_categories:
- tabular-classification
- tabular-regression
tags:
- uci
- tabular
- classification
- binary
- multiclass
- regression
size_categories:
- 10K<n<100K
- 100K<n<1M
---
# 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.csv` and `test.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 |