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arff slurped Weka

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# Instances: 1728 / # Attributes: 7
HDF5 (486.7 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

car

Version mldata

0

Comment

1. Title: Car Evaluation Database

2. Sources: (a) Creator: Marko Bohanec (b) Donors: Marko Bohanec (marko.bohanec@ijs.si) Blaz Zupan (blaz.zupan@ijs.si) (c) Date: June, 1997

3. Past Usage:

The hierarchical decision model, from which this dataset is derived, was first presented in

M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute decision making. In 8th Intl Workshop on Expert Systems and their Applications, Avignon, France. pages 59-78, 1988.

Within machine-learning, this dataset was used for the evaluation of HINT (Hierarchy INduction Tool), which was proved to be able to completely reconstruct the original hierarchical model. This, together with a comparison with C4.5, is presented in

B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by function decomposition. ICML-97, Nashville, TN. 1997 (to appear)

1. Relevant Information Paragraph:

Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX (M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.). The model evaluates cars according to the following concept structure:

CAR car acceptability . PRICE overall price . . buying buying price . . maint price of the maintenance . TECH technical characteristics . . COMFORT comfort . . . doors number of doors . . . persons capacity in terms of persons to carry . . . lug_boot the size of luggage boot . . safety estimated safety of the car

Input attributes are printed in lowercase. Besides the target concept (CAR), the model includes three intermediate concepts: PRICE, TECH, COMFORT. Every concept is in the original model related to its lower level descendants by a set of examples (for these examples sets see http://www-ai.ijs.si/BlazZupan/car.html).

The Car Evaluation Database contains examples with the structural information removed, i.e., directly relates CAR to the six input attributes: buying, maint, doors, persons, lug_boot, safety.

Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structure discovery methods.

1. Number of Instances: 1728 (instances completely cover the attribute space)

2. Number of Attributes: 6

3. Attribute Values:

buying v-high, high, med, low maint v-high, high, med, low doors 2, 3, 4, 5-more persons 2, 4, more lug_boot small, med, big safety low, med, high

1. Missing Attribute Values: none

2. Class Distribution (number of instances per class)

class N N[%]

---

unacc 1210 (70.023 %) acc 384 (22.222 %) good 69 ( 3.993 %) v-good 65 ( 3.762 %)

Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

Names

buying,maint,doors,persons,lug_boot,safety,class,

Types

1. nominal:vhigh,high,med,low
2. nominal:vhigh,high,med,low
3. nominal:2,3,4,5more
4. nominal:2,4,more
5. nominal:small,med,big
6. nominal:low,med,high
7. nominal:unacc,acc,good,vgood

Data (first 10 data points)

buying	maint	doors	pers...	lug_...	safety	class
vhigh	vhigh	2	2	small	low	unacc
vhigh	vhigh	2	2	small	med	unacc
vhigh	vhigh	2	2	small	high	unacc
vhigh	vhigh	2	2	med	low	unacc
vhigh	vhigh	2	2	med	med	unacc
vhigh	vhigh	2	2	med	high	unacc
vhigh	vhigh	2	2	big	low	unacc
vhigh	vhigh	2	2	big	med	unacc
vhigh	vhigh	2	2	big	high	unacc
vhigh	vhigh	2	4	small	low	unacc
...	...	...	...	...	...	...

Description

A gzip'ed tar containing UCI and UCI KDD datasets (uci-20070111.tar.gz, 17,952,832 Bytes)

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http://www.ics.uci.edu/~mlearn/MLRepository.html http://kdd.ics.uci.edu/

Measurement Details

Usage Scenario

revision 1

by mldata on 2010-11-06 09:58

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