The car evaluation dataset from the UCI Machine Learning Repository contains 1,728 samples and 6 categorical attributes, assessing car acceptability based on factors such as price, maintenance, and safety, making it an introductory dataset for multi-class learning.
Structured decision model dataset, suitable for learning multi-class classification and decision theory
Data generated based on a hierarchical decision model, covering evaluation dimensions such as purchase price, maintenance cost, comfort, and safety.
All 6 input features are ordered categorical variables (low/med/high/vhigh), suitable for learning ordinal encoding.
Car evaluations are classified into four levels: unacc (unacceptable), acc (acceptable), good, and vgood (very good).
70% are unacc class, with vgood only accounting for 3.8%, making it a good material for learning imbalanced classification problems.
The hierarchical decision structure makes it a perfect dataset for learning decision trees and rule extraction.
Originating from the UCI Machine Learning Repository, designed by Marko Bohanec based on decision theory.
Diverse application scenarios from basic classification to decision theory research
The hierarchical attribute structure is very suitable for learning decision tree and rule extraction algorithms
Four-class evaluation task, practice SVM, KNN, Naive Bayes, and other multi-class algorithms
Significant class imbalance, practice SMOTE, weighted loss, and other handling methods
Ordered categorical features are suitable for practicing ordinal encoding and feature engineering techniques
The following are examples of the first few rows of the car evaluation dataset
buying,maint,doors,persons,lug_boot,safety,class vhigh,vhigh,2,2,small,low,unacc vhigh,vhigh,2,2,small,med,unacc vhigh,vhigh,2,2,small,high,unacc vhigh,vhigh,2,2,med,low,unacc vhigh,vhigh,2,2,med,med,unacc vhigh,vhigh,2,2,med,high,unacc vhigh,vhigh,2,2,big,low,unacc vhigh,vhigh,2,2,big,med,unacc vhigh,vhigh,2,2,big,high,unacc
From browsing to analysis, you can start your data science project in minutes
View dataset details on the Ace Data Cloud platform, including field descriptions, sample size, and licensing information.
Download the CSV file (52 KB), data is ready to use without additional cleaning.
Use pandas.read_csv() to load the data, along with OrdinalEncoder to encode ordinal categorical features.
A classic multi-class dataset with open licensing, available for immediate download. The hierarchical decision structure makes it the best introductory dataset for decision tree learning.
