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autos

autos

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Author: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Automobile) - 1987 Please cite: 1985 Auto Imports Database This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe. The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year. Several of the attributes in the database could be used as a "class" attribute. Sources: 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037 Past Usage: Kibler,~D., Aha,~D.~W., & Albert,~M. (1989). Instance-based prediction of real-valued attributes. {it Computational Intelligence}, {it 5}, 51--57. Attribute Information: > 1. symboling: -3, -2, -1, 0, 1, 2, 3. 2. normalized-losses: continuous from 65 to 256. 3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo 4. fuel-type: diesel, gas. 5. aspiration: std, turbo. 6. num-of-doors: four, two. 7. body-style: hardtop, wagon, sedan, hatchback, convertible. 8. drive-wheels: 4wd, fwd, rwd. 9. engine-location: front, rear. 10. wheel-base: continuous from 86.6 120.9. 11. length: continuous from 141.1 to 208.1. 12. width: continuous from 60.3 to 72.3. 13. height: continuous from 47.8 to 59.8. 14. curb-weight: continuous from 1488 to 4066. 15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor. 16. num-of-cylinders: eight, five, four, six, three, twelve, two. 17. engine-size: continuous from 61 to 326. 18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi. 19. bore: continuous from 2.54 to 3.94. 20. stroke: continuous from 2.07 to 4.17. 21. compression-ratio: continuous from 7 to 23. 22. horsepower: continuous from 48 to 288. 23. peak-rpm: continuous from 4150 to 6600. 24. city-mpg: continuous from 13 to 49. 25. highway-mpg: continuous from 16 to 54. 26. price: continuous from 5118 to 45400.

26 features

symboling (target)nominal6 unique values
0 missing
engine-typenominal7 unique values
0 missing
pricenumeric186 unique values
4 missing
highway-mpgnumeric30 unique values
0 missing
city-mpgnumeric29 unique values
0 missing
peak-rpmnumeric23 unique values
2 missing
horsepowernumeric59 unique values
2 missing
compression-rationumeric32 unique values
0 missing
strokenumeric36 unique values
4 missing
borenumeric38 unique values
4 missing
fuel-systemnominal8 unique values
0 missing
engine-sizenumeric44 unique values
0 missing
num-of-cylindersnominal7 unique values
0 missing
normalized-lossesnumeric51 unique values
41 missing
curb-weightnumeric171 unique values
0 missing
heightnumeric49 unique values
0 missing
widthnumeric44 unique values
0 missing
lengthnumeric75 unique values
0 missing
wheel-basenumeric53 unique values
0 missing
engine-locationnominal2 unique values
0 missing
drive-wheelsnominal3 unique values
0 missing
body-stylenominal5 unique values
0 missing
num-of-doorsnominal2 unique values
2 missing
aspirationnominal2 unique values
0 missing
fuel-typenominal2 unique values
0 missing
makenominal22 unique values
0 missing
Show all 26 features

107 properties

NumberOfInstances
205
Number of instances (rows) of the dataset.
NumberOfFeatures
26
Number of attributes (columns) of the dataset.
NumberOfClasses
6
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
59
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
46
Number of instances with at least one value missing.
NumberOfNumericFeatures
15
Number of numeric attributes.
NumberOfSymbolicFeatures
11
Number of nominal attributes.
AutoCorrelation
0.63
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.84
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
CfsSubsetEval_DecisionStumpErrRate
0.36
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
CfsSubsetEval_DecisionStumpKappa
0.53
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
CfsSubsetEval_NaiveBayesAUC
0.84
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
CfsSubsetEval_NaiveBayesErrRate
0.36
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
CfsSubsetEval_NaiveBayesKappa
0.53
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
CfsSubsetEval_kNN1NAUC
0.84
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
CfsSubsetEval_kNN1NErrRate
0.36
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
CfsSubsetEval_kNN1NKappa
0.53
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
ClassEntropy
2.27
Entropy of the target attribute values.
DecisionStumpAUC
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.55
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.13
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
7.91
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
32.68
Percentage of instances belonging to the most frequent class.
MajorityClassSize
67
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
4.12
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
5.31
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
13207.13
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.92
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
22
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
2.61
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
7947.07
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.39
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
1.36
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
1447.09
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.29
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
3.85
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
6
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.8
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
606.99
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.11
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.83
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
3.26
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.04
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.68
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.27
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
1.46
Percentage of instances belonging to the least frequent class.
MinorityClassSize
3
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.42
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
4
Number of binary attributes.
PercentageOfBinaryFeatures
15.38
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
22.44
Percentage of instances having missing values.
PercentageOfMissingValues
1.11
Percentage of missing values.
PercentageOfNumericFeatures
57.69
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
42.31
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.63
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.04
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
25.22
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.06
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.07
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
2.44
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.19
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.58
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
98.76
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.22
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.68
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
6.89
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.8
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
2.62
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
174.05
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.42
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.39
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
41.64
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.44
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.44
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.44
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
5.8
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

20 tasks

Supervised Classification on autos
1382 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: symboling
Supervised Classification on autos
304 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: symboling
Supervised Classification on autos
294 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: symboling
Supervised Classification on autos
175 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: symboling
Supervised Classification on autos
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: symboling
Learning Curve on autos
995 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: symboling
Learning Curve on autos
76 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: symboling
Supervised Data Stream Classification on autos
26 runs - estimation_procedure: Interleaved Test then Train - target_feature: symboling
Clustering on autos
0 runs
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering - target_feature: a
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering
Clustering on autos
0 runs - estimation_procedure: 50 times Clustering
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