01_CreditScoring
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.
Nodes
- Rule Engine6 ×
- Row Filter4 ×
Column Filter3 ×- Joiner3 ×
- PMML To Cell3 ×
- Scorer3 ×
- X-Aggregator3 ×
- X-Partitioner3 ×
- Decision Tree Learner2 ×
Normalizer2 ×- RProp MLP Learner2 ×
- SVM Learner2 ×
- Bar Chart1 ×
- Category To Number1 ×
- Cell To PMML1 ×
- Concatenate1 ×
- Create Temp Dir1 ×
- Decision Tree Predictor1 ×
- File Reader1 ×
- MultiLayerPerceptron Predictor1 ×
- PMML Writer1 ×
- SVM Predictor1 ×
- Sorter1 ×
Extensions
- FeatureKNIME Base nodes
- FeatureKNIME Ensemble Learning Wrappers
- FeatureKNIME JavaScript Views
- FeatureKNIME Javasnippet