carEvaluation: Car Evaluation Database
In imptree: Classification Trees with Imprecise Probabilities

Description Usage Format Details Source References Examples

This data.frame contains the 'Car Evaluation' data set from the UCI Machine Learning Repository.
The 'Car Evaluation data' set gives the acceptance of a car directly related to the six input attributes: buying, maint, doors, persons, lug_boot, safety.

1	data(carEvaluation)

A data frame with 1728 observations on the following 7 variables, where each row contains information on one car. All variables are factor variables.

buying: Buying price of the car (Levels: high, low, med ,vhigh)
maint: Price of the maintenance (Levels: high, low, med, vhigh)
doors: Number of doors (Levels: 2, 3, 4, 5more)
persons: Capacity in terms of persons to carry (Levels: 2, 4, more)
lug_boot: Size of luggage boot (Levels: big, med, small)
safety: Estimated safety of the car (Levels: high, low, med)
acceptance: Acceptance of the car (target variable) (Levels: acc, good, unacc, vgood)

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:

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.

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.

The original data were taken from the UCI Machine Learning repository (https://archive.ics.uci.edu/ml/datasets/Car+Evaluation) and were converted into R format by Paul Fink.

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

D. Dua and E. Karra Taniskidou (2017), UCI Machine Learning Repository http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science.