orange / Orange / datasets / adult.htm

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<title>Adult Data Base</title>
<h1>Info on Adult Data Base</h1>
This data was extracted from the census bureau database found at

Donor: Ronny Kohavi and Barry Becker,
       Data Mining and Visualization
       Silicon Graphics.
       e-mail: for questions.

Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random).
48842 instances, mix of continuous and discrete    (train=32561, test=16281)
45222 if instances with unknown values are removed (train=30162, test=15060)

Duplicate or conflicting instances : 6

Class probabilities for adult.all file
Probability for the label '>50K'  : 23.93% / 24.78% (without unknowns)
Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)
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))

Prediction task is to determine whether a person makes over 50K
a year.

First cited in:
   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}}

Error Accuracy reported as follows, after removal of unknowns from
   train/test sets):
   C4.5       : 84.46+-0.30
   Naive-Bayes: 83.88+-0.30
   NBTree     : 85.90+-0.28

Following algorithms were later run with the following error rates,
all after removal of unknowns and using the original train/test split.
All these numbers are straight runs using MLC++ with default values.

   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)

Conversion of original data as follows:
1. Discretized agrossincome into two ranges with threshold 50,000.
2. Convert U.S. to US to avoid periods.
3. Convert Unknown to "?"
4. Run MLC++ GenCVFiles to generate data,test.

Description of fnlwgt (final weight): The weights on the CPS files are
controlled to independent estimates of the civilian noninstitutional
population of the US.  These are prepared monthly for us by Population
Division here at the Census Bureau.  We use 3 sets of controls. These

  1.  A single cell estimate of the population 16+ for each state.
  2.  Controls for Hispanic Origin by age and sex.
  3.  Controls by Race, age and sex.

We use all three sets of controls in our weighting program and "rake"
through them 6 times so that by the end we come back to all the
controls we used.

The term estimate refers to population totals derived from CPS by
creating "weighted tallies" of any specified socio-economic
characteristics of the population.

People with similar demographic characteristics should have similar
weights.  There is one important caveat to remember about this
statement.  That is that since the CPS sample is actually a collection
of 51 state samples, each with its own probability of selection, the
statement only applies within state.