bnlearn (version 2.0)

insurance: Insurance evaluation network data set

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 levelsFalseandTrue.
• Age(age): a three-level factor with levelsAdolescent,AdultandSenior.
• SocioEcon(socio-economic status): a four-level factor with levelsProle,Middle,UpperMiddleandWealthy.
• RiskAversion(risk aversion): a four-level factor with levelsPsychopath,Adventurous,NormalandCautious.
• VehicleYear(vehicle age): a two-level factor with levelsCurrentandolder.
• ThisCarDam(damage to this car): a four-level factor with levelsNone,Mild,ModerateandSevere.
• RuggedAuto(ruggedness of the car): a three-level factor with levelsEggShell,FootballandTank.
• Accident(severity of the accident): a four-level factor with levelsNone,Mild,ModerateandSevere.
• MakeModel(car's model): a five-level factor with levelsSportsCar,Economy,FamilySedan,LuxuryandSuperLuxury.
• DrivQuality(driving quality): a three-level factor with levelsPoor,NormalandExcellent.
• Mileage(mileage): a four-level factor with levelsFiveThou,TwentyThou,FiftyThouandDomino.
• Antilock(ABS): a two-level factor with levelsFalseandTrue.
• DrivingSkill(driving skill): a three-level factor with levelsSubStandard,NormalandExpert.
• SeniorTrain(senior training): a two-level factor with levelsFalseandTrue.
• ThisCarCost(costs for the insured car): a four-level factor with levelsThousand,TenThou,HundredThouandMillion.
• Theft(theft): a two-level factor with levelsFalseandTrue.
• CarValue(value of the car): a five-level factor with levelsFiveThou,TenThou,TwentyThou,FiftyThouandMillion.
• HomeBase(neighbourhood type): a four-level factor with levelsSecure,City,SuburbandRural.
• AntiTheft(anti-theft system): a two-level factor with levelsFalseandTrue.
• PropCost(ratio of the cost for the two cars): a four-level factor with levelsThousand,TenThou,HundredThouandMillion.
• OtherCarCost(costs for the other car): a four-level factor with levelsThousand,TenThou,HundredThouandMillion.
• OtherCar(other cars involved in the accident): a two-level factor with levelsFalseandTrue.
• MedCost(cost of the medical treatment): a four-level factor with levelsThousand,TenThou,HundredThouandMillion.
• Cushioning(cushioning): a four-level factor with levelsPoor,Fair,GoodandExcellent.
• Airbag(airbag): a two-level factor with levelsFalseandTrue.
• ILiCost(inspection cost): a four-level factor with levelsThousand,TenThou,HundredThouandMillion.
• DrivHist(driving history): a three-level factor with levelsZero,OneandMany.

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/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 = "")
# there are too many nodes for plot(), use graphviz.plot().
graphviz.plot(res)