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insurance_2

insurance_2

active ARFF Publicly available Visibility: public Uploaded 26-04-2023 by Pablo Torrijos Arenas
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  • bnlearn insurance Life Science Machine Learning sample
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Insurance Bayesian Network. Sample 2. bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#insurance) - Number of nodes: 27 - Number of arcs: 52 - Number of parameters: 1008 - Average Markov blanket size: 5.19 - Average degree: 3.85 - Maximum in-degree: 3 Authors: J. Binder, D. Koller, S. Russell, and K. Kanazawa Please cite: ([URL](https://cse.sc.edu/~mgv/csce582sp21/links/SDLC_CHILD_1993.pdf)): J. Binder, D. Koller, S. Russell, and K. Kanazawa. Adaptive Probabilistic Networks with Hidden Variables. Machine Learning, 29(2-3):213-244, 1997.

27 features

DrivHiststring3 unique values
0 missing
DrivingSkillstring3 unique values
0 missing
Agestring3 unique values
0 missing
RuggedAutostring3 unique values
0 missing
Mileagestring4 unique values
0 missing
VehicleYearstring2 unique values
0 missing
ILiCoststring4 unique values
0 missing
ThisCarDamstring4 unique values
0 missing
AntiTheftnominal2 unique values
0 missing
DrivQualitystring3 unique values
0 missing
RiskAversionstring4 unique values
0 missing
GoodStudentnominal2 unique values
0 missing
OtherCarnominal2 unique values
0 missing
MakeModelstring5 unique values
0 missing
Accidentstring4 unique values
0 missing
SocioEconstring4 unique values
0 missing
HomeBasestring4 unique values
0 missing
PropCoststring4 unique values
0 missing
CarValuestring5 unique values
0 missing
Antilocknominal2 unique values
0 missing
ThisCarCoststring4 unique values
0 missing
OtherCarCoststring3 unique values
0 missing
Theftnominal2 unique values
0 missing
Airbagnominal2 unique values
0 missing
MedCoststring4 unique values
0 missing
SeniorTrainnominal2 unique values
0 missing
Cushioningstring4 unique values
0 missing

19 properties

5000
Number of instances (rows) of the dataset.
27
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
0
Number of numeric attributes.
7
Number of nominal attributes.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
7
Number of binary attributes.
25.93
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
0.01
Number of attributes divided by the number of instances.
0
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
25.93
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.

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