Data

car

active
ARFF
Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn

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Author:
Source: Unknown -
Please cite:
1. Title: Car Evaluation Database
2. Sources:
(a) Creator: Marko Bohanec
(b) Donors: Marko Bohanec (marko.bohanec@ijs.si)
Blaz Zupan (blaz.zupan@ijs.si)
(c) Date: June, 1997
3. Past Usage:
The hierarchical decision model, from which this dataset is
derived, was first presented in
M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for
multi-attribute decision making. In 8th Intl Workshop on Expert
Systems and their Applications, Avignon, France. pages 59-78, 1988.
Within machine-learning, this dataset was used for the evaluation
of HINT (Hierarchy INduction Tool), which was proved to be able to
completely reconstruct the original hierarchical model. This,
together with a comparison with C4.5, is presented in
B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by
function decomposition. ICML-97, Nashville, TN. 1997 (to appear)
4. Relevant Information Paragraph:
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.
5. Number of Instances: 1728
(instances completely cover the attribute space)
6. Number of Attributes: 6
7. Attribute Values:
buying v-high, high, med, low
maint v-high, high, med, low
doors 2, 3, 4, 5-more
persons 2, 4, more
lug_boot small, med, big
safety low, med, high
8. Missing Attribute Values: none
9. Class Distribution (number of instances per class)
class N N[%]
-----------------------------
unacc 1210 (70.023 %)
acc 384 (22.222 %)
good 69 ( 3.993 %)
v-good 65 ( 3.762 %)
Information about the dataset
CLASSTYPE: nominal
CLASSINDEX: last

class (target) | nominal | 4 unique values 0 missing | |

buying | nominal | 4 unique values 0 missing | |

maint | nominal | 4 unique values 0 missing | |

doors | nominal | 4 unique values 0 missing | |

persons | nominal | 3 unique values 0 missing | |

lug_boot | nominal | 3 unique values 0 missing | |

safety | nominal | 3 unique values 0 missing |

0.97

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.13

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.71

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.97

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.13

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.71

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.97

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.13

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.71

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.72

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

0

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

10.54

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

0.97

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

0.71

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

0.97

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

0.71

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

0.97

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

0.26

Maximum mutual information between the nominal attributes and the target attribute.

4

The maximum number of distinct values among attributes of the nominal type.

0.11

Average mutual information between the nominal attributes and the target attribute.

14.67

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

3.57

Average number of distinct values among the attributes of the nominal type.

0

Minimal mutual information between the nominal attributes and the target attribute.

3

The minimal number of distinct values among attributes of the nominal type.

0.97

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

0.02

First quartile of mutual information between the nominal attributes and the target attribute.

First quartile of standard deviation of attributes of the numeric type.

Second quartile (Median) of kurtosis among attributes of the numeric type.

0.09

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

Second quartile (Median) of skewness among attributes of the numeric type.

Second quartile (Median) of standard deviation of attributes of the numeric type.

0.23

Third quartile of mutual information between the nominal attributes and the target attribute.

Third quartile of standard deviation of attributes of the numeric type.

0.96

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.67

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.96

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0.67

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0.96

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.67

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.88

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.17

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.61

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.88

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.17

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.61

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.88

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.17

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.61

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.53

Standard deviation of the number of distinct values among attributes of the nominal type.