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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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pretty_name: UCI Tabular Benchmark Sample |
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task_categories: |
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- tabular-classification |
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- tabular-regression |
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tags: |
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- uci |
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- tabular |
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- classification |
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- binary |
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- multiclass |
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- regression |
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size_categories: |
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- 10K<n<100K |
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- 100K<n<1M |
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--- |
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# UCI Tabular Benchmark Sample |
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This repository contains a small collection of tabular datasets mirrored from the UCI Machine Learning Repository and prepared for convenient experimentation. |
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## Contents |
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The datasets are organized by common ML task type: |
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### Binary Classification |
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Adult dataset |
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Bank Marketing dataset |
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### Multiclassification |
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Covertype dataset |
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Statlog (Shuttle) dataset |
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### Regression |
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Year Prediction MSD dataset |
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## Source and License |
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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)**. |
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- CC BY 4.0 license text: https://creativecommons.org/licenses/by/4.0/ |
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### Upstream dataset pages (UCI) |
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- Adult (Census Income): https://archive.ics.uci.edu/dataset/2/adult |
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- Bank Marketing: https://archive.ics.uci.edu/dataset/222/bank+marketing |
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- Covertype: https://archive.ics.uci.edu/dataset/31/covertype |
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- Statlog (Shuttle): https://archive.ics.uci.edu/dataset/148/statlog+shuttle |
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- YearPredictionMSD: https://archive.ics.uci.edu/dataset/203/yearpredictionmsd |
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## Modifications and Data Processing Notes |
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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. |
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Typical changes include: |
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- Adding **column headers** where the original files did not include headers. |
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- Converting original formats (for example space-separated or other delimiters) into **`.csv`**. |
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- Normalizing line endings and basic formatting fixes to improve parsing. |
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- In some cases, reorganizing files into a standard folder structure (for example `train.csv` and `test.csv`). |
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Unless explicitly stated in a dataset folder README (if present), no attempt was made to: |
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- Remove rows or features |
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- Alter feature values |
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- Rebalance classes |
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- Impute missing values |
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If you require a byte-for-byte identical copy of the upstream distribution, please download directly from the corresponding UCI page. |
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## Missing Values |
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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. |
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## Intended Use |
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This pack is intended for: |
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- Tabular model benchmarking (linear models, tree models, neural networks) |
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- Privacy and security research on tabular learning pipelines, including: |
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- reconstruction and gradient inversion attacks |
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- defenses such as clipping, noise injection, discretization, constraint-aware decoding |
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- evaluating reconstructibility using feature-level and record-level metrics |
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It is not intended for making decisions about individuals or for any high-stakes deployment |
