The Adult dataset
The information is a replica of the notes for the abalone dataset from the
UCI repository.
1. Title of Database: adult
2. Sources:
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(a) Original owners of database (name/phone/snail address/email address)
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US Census Bureau.
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(b) Donor of database (name/phone/snail address/email address)
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Ronny Kohavi and Barry Becker,
Data Mining and Visualization
Silicon Graphics.
e-mail: ronnyk@sgi.com
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(c) Date received (databases may change over time without name change!)
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05/19/96
3. Past Usage:
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(a) Complete reference of article where it was described/used
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@inproceedings{kohavi-nbtree,
author={Ron Kohavi},
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},
year = 1996,
pages={to appear}}
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(b) Indication of what attribute(s) were being predicted
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Salary greater or less than 50,000.
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(b) Indication of study's results (i.e. Is it a good domain to use?)
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Hard domain with a nice number of records.
The following results obtained using MLC++ with default settings
for the algorithms mentioned below.
| | Algorithm | Error |
| 1 | C4.5 | 15.54 |
| 2 | C4.5-auto | 14.46 |
| 3 | C4.5-rules | 14.94 |
| 4 | Voted ID3 (0.6) | 15.64 |
| 5 | Voted ID3 (0.8) | 16.47 |
| 6 | T2 | 16.84 |
| 7 | 1R | 19.54 |
| 8 | NBTree | 14.10 |
| 9 | CN2 | 16.00 |
| 10 | HOODG | 14.82 |
| 11 | FSS Naive Bayes | 14.05 |
| 12 | IDTM (Decision table) | 14.46 |
| 13 | Naive-Bayes | 16.12 |
| 14 | Nearest-neighbor (1) | 21.42 |
| 15 | Nearest-neighbor (3) | 20.35 |
| 16 | OC1 | 15.04 |
| 17 | Pebls | Crashed. Unknown why (bounds WERE increased) |
4. Relevant Information Paragraph:
Extraction was done by Barry Becker from the 1994 Census database. A set
of reasonably clean records was extracted using the following conditions:
((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
5. Number of Instances
- 48842 instances, mix of continuous and discrete (train=32561, test=16281)
- 45222 if instances with unknown values are removed (train=30162, test=15060)
- Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random).
6. Number of Attributes
6 continuous, 8 nominal attributes.
7. Attribute Information:
- age: continuous.
- workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov,
State-gov, Without-pay, Never-worked.
- fnlwgt: continuous.
- education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm,
Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th,
Preschool.
- education-num: continuous.
- marital-status: Married-civ-spouse, Divorced, Never-married, Separated,
Widowed, Married-spouse-absent, Married-AF-spouse.
- occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial,
Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical,
Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv,
Armed-Forces.
- relationship: Wife, Own-child, Husband, Not-in-family, Other-relative,
Unmarried.
- race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
- sex: Female, Male.
- capital-gain: continuous.
- capital-loss: continuous.
- hours-per-week: continuous.
- native-country: United-States, Cambodia, England, Puerto-Rico, Canada,
Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba,
Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico,
Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti,
Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia,
El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.
class: >50K, <=50K
8. Missing Attribute Values:
7% have missing values.
9. Class Distribution:
Probability for the label '>50K' : 23.93% / 24.78% (without unknowns)
Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)
10. Notes for Delve
-
One prototask (income) has been defined, using attributes 1-13 as inputs
and income level as a binary target.
- Missing values - These are confined to attributes 2 (workclass), 7
(occupation) and 14 (native-country). The prototask only uses cases with no
missing values.
- The income prototask comes with two priors, differing according to
if attribute 4 (education) is considered to be nominal or ordinal.
