imptree (version 0.5.1)

carEvaluation: Car Evaluation Database

Description

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.

Usage

data(carEvaluation)

Format

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)

Details

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

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.

References

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.

Examples

# NOT RUN {
data("carEvaluation")
summary(carEvaluation)

# }