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01_​CreditScoring

Credit Scoring

This workflow creates a credit scoring model based on historical data. As with all data mining modeling activities, it is unclear in advance which analytic method is most suitable. This workflow therefore uses three different methods simultaneously – Decision Trees, Neural Networking and SVM – then automatically determines which model is most accurate and writes that model out for further use. As a preprocessing step, this workflow converts nominals to numerics so the data are suitable for a variety of modeling techniques. Each model comes from a Learn / Test process including cross validation to ensure model stability. The workflow writes out the best performing model in the official PMML format, so that other applications can use the model.

Write out best model Create different models Show accuracies This workflow demonstrates how to generate different predictive models and evaluate them with cross validation. Sort by AccuracyOnly first rowGerman credit data Cross Validation(Decision Tree) Cross Validation(Neural Network) Cross Validation(SVM) Sorter Row Filter Cell To PMML PMML Writer Create Temp Dir File Reader Category To Number Bar Chart Concatenate Write out best model Create different models Show accuracies This workflow demonstrates how to generate different predictive models and evaluate them with cross validation. Sort by AccuracyOnly first rowGerman credit data Cross Validation(Decision Tree) Cross Validation(Neural Network) Cross Validation(SVM) Sorter Row Filter Cell To PMML PMML Writer Create Temp Dir File Reader Category To Number Bar Chart Concatenate

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