Car Evaluation Data Set

from: https://archive.ics.uci.edu/ml/datasets/car+evaluation

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)

4. 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.

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

6. Number of Attributes: 6

7. 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

8. Missing Attribute Values: none

9. 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 %)