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auto_price

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

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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.
The original data (from the UCI repository) (http://www.ics.uci.edu/~mlearn/MLSummary.html) has 205 instances
described by 26 attributes :
- 15 continuous
- 1 integer
- 10 nominal
The following provides more information on these attributes:
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.
The original data also has some 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
I've changed the original data in the following way :
- All instances with unknowns were removed giving 159 instances.
- The goal variable is "price"
- All nominal attributes (10) were removed.
Original source: UCI machine learning repository. (http://www.ics.uci.edu/~mlearn/MLSummary.html).
Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at
http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html
Characteristics: 159 cases; 14 continuous variables; 1 nominal vars..

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

symboling | nominal | 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 |

6

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

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

2.53

Third quartile of kurtosis among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

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.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.trees.RandomTree -depth 2

Third quartile of mutual information between the nominal attributes and the target attribute.

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

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.92

Third quartile of skewness among attributes of the numeric type.

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

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

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

0.15

First quartile of kurtosis among attributes of the numeric type.

35.65

Third quartile of standard deviation of attributes of the numeric type.

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

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

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

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

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

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

First quartile of mutual information between the nominal attributes and the target attribute.

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

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

6

Average number of distinct values among the attributes of the nominal type.

0.16

First quartile of skewness among attributes of the numeric type.

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

0

Standard deviation of the number of distinct values among attributes of the nominal type.

2.27

First quartile of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

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

0.64

Second quartile (Median) of kurtosis among attributes of the numeric type.

95.84

Second quartile (Median) of means among attributes of the numeric type.

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.DecisionStump

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

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

0.78

Second quartile (Median) of skewness among attributes of the numeric type.

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

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

6

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

6.46

Second quartile (Median) of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1