90 lines
4.2 KiB
Plaintext
90 lines
4.2 KiB
Plaintext
1. Title of Database: adult
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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,
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Data Mining and Visualization
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Silicon Graphics.
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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
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3. Past Usage:
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(a) Complete reference of article where it was described/used
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@inproceedings{kohavi-nbtree,
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author={Ron Kohavi},
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title={Scaling Up the Accuracy of Naive-Bayes Classifiers: a
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Decision-Tree Hybrid},
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booktitle={Proceedings of the Second International Conference on
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Knowledge Discovery and Data Mining},
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year = 1996,
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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.
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The following results obtained using MLC++ with default settings
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for the algorithms mentioned below.
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Algorithm Error
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-- ---------------- -----
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1 C4.5 15.54
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2 C4.5-auto 14.46
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3 C4.5 rules 14.94
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4 Voted ID3 (0.6) 15.64
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5 Voted ID3 (0.8) 16.47
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6 T2 16.84
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7 1R 19.54
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8 NBTree 14.10
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9 CN2 16.00
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10 HOODG 14.82
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11 FSS Naive Bayes 14.05
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12 IDTM (Decision table) 14.46
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13 Naive-Bayes 16.12
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14 Nearest-neighbor (1) 21.42
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15 Nearest-neighbor (3) 20.35
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16 OC1 15.04
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17 Pebls Crashed. Unknown why (bounds WERE increased)
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4. Relevant Information Paragraph:
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Extraction was done by Barry Becker from the 1994 Census database. A set
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of reasonably clean records was extracted using the following conditions:
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((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
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5. Number of Instances
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48842 instances, mix of continuous and discrete (train=32561, test=16281)
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45222 if instances with unknown values are removed (train=30162, test=15060)
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Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random).
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6. Number of Attributes
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6 continuous, 8 nominal attributes.
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7. Attribute Information:
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age: continuous.
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workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
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fnlwgt: continuous.
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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.
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education-num: continuous.
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marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
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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.
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relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
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race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
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sex: Female, Male.
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capital-gain: continuous.
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capital-loss: continuous.
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hours-per-week: continuous.
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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.
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class: >50K, <=50K
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8. Missing Attribute Values:
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7% have missing values.
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9. Class Distribution:
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Probability for the label '>50K' : 23.93% / 24.78% (without unknowns)
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Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)
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