Data

autoPrice

active
ARFF
Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn

0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes

0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes

Issue | #Downvotes for this reason | By |
---|

Loading wiki

Help us complete this description
Edit

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%

class (target) | numeric | 145 unique values 0 missing | |

symboling | numeric | 6 unique values 0 missing | |

normalized-losses | numeric | 51 unique values 0 missing | |

wheel-base | numeric | 40 unique values 0 missing | |

length | numeric | 56 unique values 0 missing | |

width | numeric | 33 unique values 0 missing | |

height | numeric | 39 unique values 0 missing | |

curb-weight | numeric | 136 unique values 0 missing | |

engine-size | numeric | 32 unique values 0 missing | |

bore | numeric | 33 unique values 0 missing | |

stroke | numeric | 31 unique values 0 missing | |

compression-ratio | numeric | 29 unique values 0 missing | |

horsepower | numeric | 48 unique values 0 missing | |

peak-rpm | numeric | 20 unique values 0 missing | |

city-mpg | numeric | 25 unique values 0 missing | |

highway-mpg | numeric | 28 unique values 0 missing |

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

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

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

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

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

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

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

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

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

-0.12

First quartile of kurtosis 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.

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.

2.19

Third quartile of kurtosis 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

Area Under the ROC Curve 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

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.