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:

(a) Original owners of database (name/phone/snail address/email address)
US Census Bureau.

(b) Donor of database (name/phone/snail address/email address)
Ronny Kohavi and Barry Becker,
Data Mining and Visualization
Silicon Graphics.

(c) Date received (databases may change over time without name change!)

3. Past Usage:

(a) Complete reference of article where it was described/used
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}}
(b) Indication of what attribute(s) were being predicted
Salary greater or less than 50,000.
(b) Indication of study's results (i.e. Is it a good domain to use?)
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

6. Number of Attributes

6 continuous, 8 nominal attributes.

7. Attribute Information:

  1. age: continuous.
  2. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
  3. fnlwgt: continuous.
  4. 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.
  5. education-num: continuous.
  6. marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
  7. 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.
  8. relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
  9. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
  10. sex: Female, Male.
  11. capital-gain: continuous.
  12. capital-loss: continuous.
  13. hours-per-week: continuous.
  14. 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.
  15. 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

  1. One prototask (income) has been defined, using attributes 1-13 as inputs and income level as a binary target.
  2. Missing values - These are confined to attributes 2 (workclass), 7 (occupation) and 14 (native-country). The prototask only uses cases with no missing values.
  3. The income prototask comes with two priors, differing according to if attribute 4 (education) is considered to be nominal or ordinal.

Last Updated 8 October 1996
Comments and questions to: