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Insurance Company Benchmark (COIL 2000) Data Set

Below are papers that cite this data set, with context shown. Papers were automatically harvested and associated with this data set, in collaboration with

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Bianca Zadrozny and Charles Elkan. Transforming classifier scores into accurate multiclass probability estimates. KDD. 2002.

PAV to binning with bin sizes varying from 5 to 50. Although we did not have to set any parameters for the PAV method, it performed comparably to the best parameter setting for binning. The next dataset we use is The Insurance Company Benchmark (TIC), also known as the COIL 2000 dataset, which is available in the UCI KDD repository [3]. The decision-making task is analogous to the KDD-98 task:

Stephen D. Bay and Dennis F. Kibler and Michael J. Pazzani and Padhraic Smyth. The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. SIGKDD Explorations, 2. 2000.

currently in the archive. Classification: predict the value of a categorical target variable. For example, the Insurance Benchmark data set was used to predict which customers were interested in buying an insurance policy based on product usage data and demographic information. Regression: predict the value of a continuous target

Stefan R uping. A Simple Method For Estimating Conditional Probabilities For SVMs. CS Department, AI Unit Dortmund University.

a business cycle analysis problem (business), an analysis of a direct mailing application (directmailing), a data set from a life insurance company (insurance) and intensive care patient monitoring data (medicine). Prior to learning, nominal attributes were binarised and the attributes were scaled to expectancy 0

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