car_eval: Car Evaluation

Description Usage Format Details Source References

Description

Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX.

Usage

1

Format

A data frame with 1728 observations on the following 7 variables.

  1. buying

  2. maint

  3. doors

  4. persons

  5. lug_boot

  6. safety

  7. class

Details

Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods.

Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX. The model evaluates cars according to the following concept structure:

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.

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.

Source

Creator: Marko Bohanec

Donors:

  1. Marko Bohanec (marko.bohanec '@' ijs.si)

  2. Blaz Zupan (blaz.zupan '@' ijs.si)

References

M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.)

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.

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

https://archive.ics.uci.edu/ml/machine-learning-databases/car/

https://archive.ics.uci.edu/ml/datasets/Car+Evaluation


tyluRp/ucimlr documentation built on May 17, 2019, 1:15 a.m.