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
Multivariate
Subject Area
Other
Associated Tasks
Classification
Feature Type
Categorical
# Instances
1728
# Features
6
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 http://www-ai.ijs.si/BlazZupan/car.html). 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?
No
Introductory Paper
By M. Bohanec, V. Rajkovič. 1988
Published in 8th Intl Workshop on Expert Systems and their Applications, Avignon, France
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| buying | Feature | Categorical | buying price | no | |
| maint | Feature | Categorical | price of the maintenance | no | |
| doors | Feature | Categorical | number of doors | no | |
| persons | Feature | Categorical | capacity in terms of persons to carry | no | |
| lug_boot | Feature | Categorical | the size of luggage boot | no | |
| safety | Feature | Categorical | estimated safety of the car | no | |
| class | Target | Categorical | evaulation level (unacceptable, acceptable, good, very good) | no |
0 to 7 of 7
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
Dataset Files
| File | Size |
|---|---|
| car.data | 50.7 KB |
| car.names | 3 KB |
| car.c45-names | 276 Bytes |
Papers Citing this Dataset
Sort by Year, desc
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.
By Eric Keiji, Gabriel Ferreira, Claudio Silva, Roberto Cesar. 2017
Published in ArXiv.
By Vahid Jalali, David Leake, Najmeh Forouzandehmehr. 2017
Published in IJCAI.
By Kedar Potdar, Taher Pardawala, Chinmay Pai. 2017
Published in International Journal of Computer Applications.
0 to 5 of 34
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset car_evaluation = fetch_ucirepo(id=19) # data (as pandas dataframes) X = car_evaluation.data.features y = car_evaluation.data.targets # metadata print(car_evaluation.metadata) # variable information print(car_evaluation.variables)
Bohanec, M. (1988). Car Evaluation [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5JP48.
Keywords
Creators
Marko Bohanec
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.