Car Evaluation

Donated on 5/31/1997

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

Dataset Characteristics


Subject Area


Associated Tasks


Feature Type


# Instances


# Features


Dataset Information

Additional Information

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 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.

Has Missing Values?


Introductory Paper

Knowledge acquisition and explanation for multi-attribute decision making

By M. Bohanec, V. Rajkovič. 1988

Published in 8th Intl Workshop on Expert Systems and their Applications, Avignon, France

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
buyingFeatureCategoricalbuying priceno
maintFeatureCategoricalprice of the maintenanceno
doorsFeatureCategoricalnumber of doorsno
personsFeatureCategoricalcapacity in terms of persons to carryno
lug_bootFeatureCategoricalthe size of luggage bootno
safetyFeatureCategoricalestimated safety of the carno
classTargetCategoricalevaulation level (unacceptable, acceptable, good, very good)no

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Additional Variable Information

buying: vhigh, high, med, low. maint: vhigh, high, med, low. doors: 2, 3, 4, 5more. persons: 2, 4, more. lug_boot: small, med, big. safety: low, med, high.

Class Labels

unacc, acc, good, vgood

Baseline Model Performance

Papers Citing this Dataset

GluonTS: Probabilistic Time Series Models in Python

By Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Turkmen, Yuyang Wang. 2019

Published in ArXiv.

Optimal Sparse Decision Trees

By Xiyang Hu, Cynthia Rudin, Margo Seltzer. 2019

Published in ArXiv.

A New Urban Objects Detection Framework Using Weakly Annotated Sets

By Eric Keiji, Gabriel Ferreira, Claudio Silva, Roberto Cesar. 2017

Published in ArXiv.

Learning and Applying Case Adaptation Rules for Classification: An Ensemble Approach

By Vahid Jalali, David Leake, Najmeh Forouzandehmehr. 2017

Published in IJCAI.

A Comparative Study of Categorical Variable Encoding Techniques for Neural Network Classifiers

By Kedar Potdar, Taher Pardawala, Chinmay Pai. 2017

Published in International Journal of Computer Applications.

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34 citations




Marko Bohanec


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