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insurance {bnlearn} R Documentation

Insurance evaluation network (synthetic) 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 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 https://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.

Examples

# load the data.
data(insurance)
# create and plot the network structure.
modelstring = paste0("[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]")
dag = model2network(modelstring)
## Not run: graphviz.plot(dag, shape = "ellipse")
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