automobile: Automobile
In tyluRp/ucimlr: UCI Machine Learning Repository
Description
Usage
Format
Details
Source
References
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.
Usage
1
Format
A data frame with 205 observations on the following 26 variables.
symboling
normalized-losses
make
fuel-type
aspiration
num-of-doors
body-style
drive-wheels
engine-location
wheel-base
length
width
height
curb-weight
engine-type
num-of-cylinders
engine-size
fuel-system
bore
stroke
compression-ratio
horsepower
peak-rpm
city-mpg
highway-mpg
price
Details
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.
From 1985 Ward's Automotive Yearbook.
Source
Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
Date: 19 May 1987
Sources:
1985 Model Import Car and Truck Specifications, 1985 Ward's
Automotive Yearbook.
Personal Auto Manuals, Insurance Services Office, 160 Water Street,
New York, NY 10038
Insurance Collision Report, Insurance Institute for Highway Safety,
Watergate 600, Washington, DC 20037
References
Kibler, D., Aha, D.W., & Albert,M. (1989). Instance-based prediction of
real-valued attributes. Computational Intelligence, Vol 5, 51–57.
https://archive.ics.uci.edu/ml/machine-learning-databases/autos/
https://archive.ics.uci.edu/ml/datasets/Automobile
tyluRp/ucimlr documentation built on Feb. 2, 2021, 6:51 a.m.
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Description Usage Format Details Source References
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.
Usage
1 |
Format
A data frame with 205 observations on the following 26 variables.
symboling
normalized-losses
make
fuel-type
aspiration
num-of-doors
body-style
drive-wheels
engine-location
wheel-base
length
width
height
curb-weight
engine-type
num-of-cylinders
engine-size
fuel-system
bore
stroke
compression-ratio
horsepower
peak-rpm
city-mpg
highway-mpg
price
Details
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.
From 1985 Ward's Automotive Yearbook.
Source
Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
Date: 19 May 1987
Sources:
1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook.
Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038
Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037
References
Kibler, D., Aha, D.W., & Albert,M. (1989). Instance-based prediction of real-valued attributes. Computational Intelligence, Vol 5, 51–57.
https://archive.ics.uci.edu/ml/machine-learning-databases/autos/
https://archive.ics.uci.edu/ml/datasets/Automobile
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