Data
autos

autos

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
1 likes downloaded by 24 people , 26 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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
normalized-lossesnumeric51 unique values
41 missing
makenominal22 unique values
0 missing
fuel-typenominal2 unique values
0 missing
aspirationnominal2 unique values
0 missing
num-of-doorsnominal2 unique values
2 missing
body-stylenominal5 unique values
0 missing
drive-wheelsnominal3 unique values
0 missing
engine-locationnominal2 unique values
0 missing
wheel-basenumeric53 unique values
0 missing
lengthnumeric75 unique values
0 missing
widthnumeric44 unique values
0 missing
heightnumeric49 unique values
0 missing
curb-weightnumeric171 unique values
0 missing
engine-typenominal7 unique values
0 missing
num-of-cylindersnominal7 unique values
0 missing
engine-sizenumeric44 unique values
0 missing
fuel-systemnominal8 unique values
0 missing
borenumeric38 unique values
4 missing
strokenumeric36 unique values
4 missing
compression-rationumeric32 unique values
0 missing
horsepowernumeric59 unique values
2 missing
peak-rpmnumeric23 unique values
2 missing
city-mpgnumeric29 unique values
0 missing
highway-mpgnumeric30 unique values
0 missing
pricenumeric186 unique values
4 missing

19 properties

205
Number of instances (rows) of the dataset.
26
Number of attributes (columns) of the dataset.
6
Number of distinct values of the target attribute (if it is nominal).
59
Number of missing values in the dataset.
46
Number of instances with at least one value missing.
15
Number of numeric attributes.
11
Number of nominal attributes.
22.44
Percentage of instances having missing values.
0.63
Average class difference between consecutive instances.
1.11
Percentage of missing values.
0.13
Number of attributes divided by the number of instances.
57.69
Percentage of numeric attributes.
32.68
Percentage of instances belonging to the most frequent class.
42.31
Percentage of nominal attributes.
67
Number of instances belonging to the most frequent class.
1.46
Percentage of instances belonging to the least frequent class.
3
Number of instances belonging to the least frequent class.
4
Number of binary attributes.
15.38
Percentage of binary attributes.

10 tasks

641 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: symboling
305 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: symboling
295 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: symboling
176 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: symboling
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: symboling
995 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: symboling
76 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: symboling
26 runs - estimation_procedure: Interleaved Test then Train - target_feature: symboling
Define a new task