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autoPrice

autoPrice

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Author: Source: Unknown - Please cite: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! All nominal attributes and instances with missing values are deleted. Price treated as the class attribute. As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction using instance-based learning with encoding length selection. In Progress in Connectionist-Based Information Systems. Singapore: Springer-Verlag. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1. Title: 1985 Auto Imports Database 2. Source Information: -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) -- Date: 19 May 1987 -- 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 3. Past Usage: -- Kibler,~D., Aha,~D.~W., & Albert,~M. (1989). Instance-based prediction of real-valued attributes. {it Computational Intelligence}, {it 5}, 51--57. -- Predicted price of car using all numeric and Boolean attributes -- Method: an instance-based learning (IBL) algorithm derived from a localized k-nearest neighbor algorithm. Compared with a linear regression prediction...so all instances with missing attribute values were discarded. This resulted with a training set of 159 instances, which was also used as a test set (minus the actual instance during testing). -- Results: Percent Average Deviation Error of Prediction from Actual -- 11.84% for the IBL algorithm -- 14.12% for the resulting linear regression equation 4. Relevant Information: -- Description 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. -- Note: Several of the attributes in the database could be used as a "class" attribute. 5. Number of Instances: 205 6. Number of Attributes: 26 total -- 15 continuous -- 1 integer -- 10 nominal 7. Attribute Information: Attribute: Attribute Range: ------------------ ----------------------------------------------- 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. 8. Missing Attribute Values: (denoted by "?") Attribute #: Number of instances missing a value: 2. 41 6. 2 19. 4 20. 4 22. 2 23. 2 26. 4%

16 features

class (target)numeric145 unique values
0 missing
symbolingnumeric6 unique values
0 missing
normalized-lossesnumeric51 unique values
0 missing
wheel-basenumeric40 unique values
0 missing
lengthnumeric56 unique values
0 missing
widthnumeric33 unique values
0 missing
heightnumeric39 unique values
0 missing
curb-weightnumeric136 unique values
0 missing
engine-sizenumeric32 unique values
0 missing
borenumeric33 unique values
0 missing
strokenumeric31 unique values
0 missing
compression-rationumeric29 unique values
0 missing
horsepowernumeric48 unique values
0 missing
peak-rpmnumeric20 unique values
0 missing
city-mpgnumeric25 unique values
0 missing
highway-mpgnumeric28 unique values
0 missing

107 properties

159
Number of instances (rows) of the dataset.
16
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
16
Number of numeric attributes.
0
Number of nominal attributes.
-2423.07
Average class difference between consecutive instances.
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
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
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
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
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
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
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
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
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
Entropy of the target attribute values.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.1
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
5.73
Maximum kurtosis among attributes of the numeric type.
11445.73
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
2.71
Maximum skewness among attributes of the numeric type.
5877.86
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
1.06
Mean kurtosis among attributes of the numeric type.
1238.95
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Average number of distinct values among the attributes of the nominal type.
0.69
Mean skewness among attributes of the numeric type.
435.09
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-0.82
Minimum kurtosis among attributes of the numeric type.
0.74
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
-0.99
Minimum skewness among attributes of the numeric type.
0.27
Minimum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
100
Percentage of numeric attributes.
0
Percentage of nominal attributes.
First quartile of entropy among attributes.
-0.12
First quartile of kurtosis among attributes of the numeric type.
14.25
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
0.15
First quartile of skewness among attributes of the numeric type.
2.03
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.63
Second quartile (Median) of kurtosis among attributes of the numeric type.
80.72
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.76
Second quartile (Median) of skewness among attributes of the numeric type.
6.28
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
2.19
Third quartile of kurtosis among attributes of the numeric type.
159.59
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.92
Third quartile of skewness among attributes of the numeric type.
34.42
Third quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Standard deviation of the number of distinct values among attributes of the nominal type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

7 tasks

0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
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