Summary: Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods.
| Parameter | Value |
|---|---|
| Name | Car Evaluation |
| Labeled | Yes |
| Time Series | No |
| Simulation | No |
| Missing Values | No |
| Dataset Characteristics | Multivariate |
| Feature Type | Categorical |
| Associated Tasks | Classification |
| Number of Instances | 1728 |
| Number of Features | 6 |
| Date Donated | 1997-05-31 |
| Source | UCI Machine Learning Repository |
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.
Automobile evaluation, Decision-making, Categorical data, Multivariate data, Classification task