task_categories:tabular-classification size_categories:10K<n<100K language:en license:cc adult tabular_classification binary_classification multiclass_classification UCI region:us
@inproceedings{DBLP:conf/kdd/Kohavi96,
  author    = {Ron Kohavi},
  editor    = {Evangelos Simoudis and
               Jiawei Han and
               Usama M. Fayyad},
  title     = {Scaling Up the Accuracy of Naive-Bayes Classifiers: {A} Decision-Tree
               Hybrid},
  booktitle = {Proceedings of the Second International Conference on Knowledge Discovery
               and Data Mining (KDD-96), Portland, Oregon, {USA}},
  pages     = {202--207},
  publisher = {{AAAI} Press},
  year      = {1996},
  url       = {http://www.aaai.org/Library/KDD/1996/kdd96-033.php},
  timestamp = {Mon, 05 Jun 2017 13:20:21 +0200},
  biburl    = {https://dblp.org/rec/conf/kdd/Kohavi96.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
---
language:
- en
tags:
- adult
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Adult
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- encoding
- income
- income-no race
- race
license: cc
---
# Adult
The [Adult dataset](https://archive.ics.uci.edu/ml/datasets/Adult) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Census dataset including personal characteristic of a person, and their income threshold.

# Configurations and tasks
| **Configuration** | **Task**                  | Description                                                     |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding          |                           | Encoding dictionary showing original values of encoded features.|
| income            | Binary classification     | Classify the person's income as over or under the threshold.    |
| income-no race    | Binary classification     | As `income`, but the `race` feature is removed.                 |
| race              | Multiclass classification | Predict the race of the individual.                             |

# Usage
```python
from datasets import load_dataset

dataset = load_dataset("mstz/adult", "income")["train"]
```

# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.

|**Feature**                    |**Type**   | **Description**                                            |
|-------------------------------|-----------|------------------------------------------------------------|
|`age`                          |`[int64]`  | Age of the person.                                         |
|`capital_gain`                 |`[float64]`| Capital gained by the person.                              |
|`capital_loss`                 |`[float64]`| Capital lost by the person.                                |
|`education`                    |`[int8]`   | Education level: the higher, the more educated the person. |
|`final_weight`                 |`[int64]`  |                                                            |
|`hours_worked_per_week`        |`[int64]`  | Hours worked per week.                                     |
|`marital_status`               |`[string]` | Marital status of the person.                              |
|`native_country`               |`[string]` | Native country of the person.                              |
|`occupation`                   |`[string]` | Job of the person.                                         |
|`race`                         |`[string]` | Race of the person.                                        |
|`relationship`                 |`[string]` |                                                            |
|`is_male`                      |`[bool]`   | Man/Woman.                                                 |
|`workclass`                    |`[string]` | Type of job of the person.                                 |
|**over_threshold**             |`int8`     | `1` for income `>= 50k$`, `0` otherwise.                   |