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")

[Package bnlearn version 4.7.1 Index]