insurance: Insurance evaluation network (synthetic) data set

In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference

Description

Insurance is a network for evaluating car insurance risks.

Usage

data(insurance)

Format

The insurance data set contains the following 27 variables:

• GoodStudent (good student): a two-level factor with levels False and True.

• Age (age): a three-level factor with levels Adolescent, Adult and Senior.

• SocioEcon (socio-economic status): a four-level factor with levels Prole, Middle, UpperMiddle and Wealthy.

• RiskAversion (risk aversion): a four-level factor with levels Psychopath, Adventurous, Normal and Cautious.

• VehicleYear (vehicle age): a two-level factor with levels Current and older.

• ThisCarDam (damage to this car): a four-level factor with levels None, Mild, Moderate and Severe.

• RuggedAuto (ruggedness of the car): a three-level factor with levels EggShell, Football and Tank.

• Accident (severity of the accident): a four-level factor with levels None, Mild, Moderate and Severe.

• MakeModel (car's model): a five-level factor with levels SportsCar, Economy, FamilySedan, Luxury and SuperLuxury.

• DrivQuality (driving quality): a three-level factor with levels Poor, Normal and Excellent.

• Mileage (mileage): a four-level factor with levels FiveThou, TwentyThou, FiftyThou and Domino.

• Antilock (ABS): a two-level factor with levels False and True.

• DrivingSkill (driving skill): a three-level factor with levels SubStandard, Normal and Expert.

• SeniorTrain (senior training): a two-level factor with levels False and True.

• ThisCarCost (costs for the insured car): a four-level factor with levels Thousand, TenThou, HundredThou and Million.

• Theft (theft): a two-level factor with levels False and True.

• CarValue (value of the car): a five-level factor with levels FiveThou, TenThou, TwentyThou, FiftyThou and Million.

• HomeBase (neighbourhood type): a four-level factor with levels Secure, City, Suburb and Rural.

• AntiTheft (anti-theft system): a two-level factor with levels False and True.

• PropCost (ratio of the cost for the two cars): a four-level factor with levels Thousand, TenThou, HundredThou and Million.

• OtherCarCost (costs for the other car): a four-level factor with levels Thousand, TenThou, HundredThou and Million.

• OtherCar (other cars involved in the accident): a two-level factor with levels False and True.

• MedCost (cost of the medical treatment): a four-level factor with levels Thousand, TenThou, HundredThou and Million.

• Cushioning (cushioning): a four-level factor with levels Poor, Fair, Good and Excellent.

• Airbag (airbag): a two-level factor with levels False and True.

• ILiCost (inspection cost): a four-level factor with levels Thousand, TenThou, HundredThou and Million.

• DrivHist (driving history): a three-level factor with levels Zero, One and Many.

Note

The complete BN can be downloaded from http://www.bnlearn.com/bnrepository.

Source

Binder J, Koller D, Russell S, Kanazawa K (1997). "Adaptive Probabilistic Networks with Hidden Variables". Machine Learning, 29(2-3), 213-244.

Elidan G (2001). "Bayesian Network Repository".
http://www.cs.huji.ac.il/site/labs/compbio/Repository.

Examples

# load the data and build the correct network from the model string.
data(insurance)
res = empty.graph(names(insurance))
modelstring(res) = paste("[Age][Mileage][SocioEcon|Age][GoodStudent|Age:SocioEcon]",
  "[RiskAversion|Age:SocioEcon][OtherCar|SocioEcon][VehicleYear|SocioEcon:RiskAversion]",
  "[MakeModel|SocioEcon:RiskAversion][SeniorTrain|Age:RiskAversion]",
  "[HomeBase|SocioEcon:RiskAversion][AntiTheft|SocioEcon:RiskAversion]",
  "[RuggedAuto|VehicleYear:MakeModel][Antilock|VehicleYear:MakeModel]",
  "[DrivingSkill|Age:SeniorTrain][CarValue|VehicleYear:MakeModel:Mileage]",
  "[Airbag|VehicleYear:MakeModel][DrivQuality|RiskAversion:DrivingSkill]",
  "[Theft|CarValue:HomeBase:AntiTheft][Cushioning|RuggedAuto:Airbag]",
  "[DrivHist|RiskAversion:DrivingSkill][Accident|DrivQuality:Mileage:Antilock]",
  "[ThisCarDam|RuggedAuto:Accident][OtherCarCost|RuggedAuto:Accident]",
  "[MedCost|Age:Accident:Cushioning][ILiCost|Accident]",
  "[ThisCarCost|ThisCarDam:Theft:CarValue][PropCost|ThisCarCost:OtherCarCost]",
  sep = "")
## Not run: 
# there are too many nodes for plot(), use graphviz.plot().
graphviz.plot(res)
## End(Not run)

vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.