Data
freMTPL2freq

freMTPL2freq

active ARFF Public Domain (CC0) Visibility: public Uploaded 05-11-2018 by Christophe Dutang
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  • freMTPL2freq Life Science Machine Learning
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The dataset freMTPL2freq contains risk features for 677,991 motor third-part liability policies (observed mostly on one year). See https://github.com/dutangc/CASdatasets for more details. The dataset is associated with 'Computational Actuarial Science with R' edited by Arthur Charpentier, CRC, 2018.

12 features

IDpolnumeric678013 unique values
0 missing
ClaimNbnumeric11 unique values
0 missing
Exposurenumeric181 unique values
0 missing
Areanominal6 unique values
0 missing
VehPowernumeric12 unique values
0 missing
VehAgenumeric78 unique values
0 missing
DrivAgenumeric83 unique values
0 missing
BonusMalusnumeric115 unique values
0 missing
VehBrandnominal11 unique values
0 missing
VehGasstring2 unique values
0 missing
Densitynumeric1607 unique values
0 missing
Regionnominal22 unique values
0 missing

19 properties

678013
Number of instances (rows) of the dataset.
12
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.
8
Number of numeric attributes.
3
Number of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
66.67
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
25
Percentage of nominal attributes.

9 tasks

0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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