carEvaluation {imptree} | R Documentation |
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 |
. . 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.
Source
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.
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
data("carEvaluation")
summary(carEvaluation)