Data
car

car

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  • Computer Systems Economics OpenML-CC18 study_135 study_144 study_218 study_98 study_99 uci study_271 study_240 study_253 study_285 study_446 study_447 study_448 study_449 study_275
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Author: Marko Bohanec, Blaz Zupan Source: [UCI](https://archive.ics.uci.edu/ml/datasets/car+evaluation) - 1997 Please cite: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html) Car Evaluation Database This 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. ### Changes with respect to car (1) The ordinal variables are stored as ordered factors in this version. ### Relevant papers: 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. M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.

7 features

class (target)nominal4 unique values
0 missing
buyingnominal4 unique values
0 missing
maintnominal4 unique values
0 missing
doorsnominal4 unique values
0 missing
personsnominal3 unique values
0 missing
lug_bootnominal3 unique values
0 missing
safetynominal3 unique values
0 missing
Show all 7 features

62 properties

NumberOfInstances
1728
Number of instances (rows) of the dataset.
NumberOfFeatures
7
Number of attributes (columns) of the dataset.
NumberOfClasses
4
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
0
Number of numeric attributes.
NumberOfSymbolicFeatures
7
Number of nominal attributes.
MinNominalAttDistinctValues
3
The minimal number of distinct values among attributes of the nominal type.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
MaxMutualInformation
0.26
Maximum mutual information between the nominal attributes and the target attribute.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile3MutualInformation
0.23
Third quartile of mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
4
The maximum number of distinct values among attributes of the nominal type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
1.58
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MinorityClassPercentage
3.76
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MinorityClassSize
65
Number of instances belonging to the least frequent class.
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0.53
Standard deviation of the number of distinct values among attributes of the nominal type.
MeanAttributeEntropy
1.79
Average entropy of the attributes.
NumberOfBinaryFeatures
0
Number of binary attributes.
Quartile1MutualInformation
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.6
Average class difference between consecutive instances.
MeanMutualInformation
0.11
Average mutual information between the nominal attributes and the target attribute.
Quartile2AttributeEntropy
1.79
Second quartile (Median) of entropy among attributes.
ClassEntropy
1.21
Entropy of the target attribute values.
MeanNoiseToSignalRatio
14.67
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Dimensionality
0
Number of attributes divided by the number of instances.
MeanNominalAttDistinctValues
3.57
Average number of distinct values among the attributes of the nominal type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
EquivalentNumberOfAtts
10.54
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
Quartile2MutualInformation
0.09
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassPercentage
70.02
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
MajorityClassSize
1210
Number of instances belonging to the most frequent class.
MinAttributeEntropy
1.58
Minimal entropy among attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxAttributeEntropy
2
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
2
Third quartile of entropy among attributes.
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
Show all 62 properties

26 tasks

Supervised Classification on car
7180 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on car
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
Supervised Classification on car
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on car
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on car
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on car
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Supervised Classification on car
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on car
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: classification problem
Learning Curve on car
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on car
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on car
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on car
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on car
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on car
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on car
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on car
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on car
0 runs - estimation_procedure: 50 times Clustering
Clustering on car
0 runs - estimation_procedure: 50 times Clustering
Clustering on car
0 runs - estimation_procedure: 50 times Clustering
Clustering on car
0 runs - estimation_procedure: 50 times Clustering
Clustering on car
0 runs
Clustering on car
0 runs - estimation_procedure: 50 times Clustering
Clustering on car
0 runs - estimation_procedure: 50 times Clustering
Clustering on car
0 runs - estimation_procedure: 50 times Clustering
Clustering on car
0 runs - estimation_procedure: 50 times Clustering
Clustering on car
0 runs - estimation_procedure: 50 times Clustering
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