02_​Partial_​Dependence_​Pre-processing

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This is an example for visualizing a partial dependence plot and an ICE curves plot in KNIME.
An XGBoost model was picked, but any model and its set of Learner and Predictor nodes can be used.

- Read the dataset about wines
- Partition the data in train and test
- Create the samples via the apposite shared component Partial Dependence Pre-processing
- Score the samples using the predictor node and a trained model
- Visualize the Partial Dependence/ICE Plot and customize it directly in the View (Execute > Right Click > Open View)
- Apply and Close to save the custom in-view settings

URL: Data Source: UCI - Wine Quality Data Set http://archive.ics.uci.edu/ml/datasets/wine+quality
URL: Christoph Molnar - Interpretable Machine Learning - A Guide for Making Black Box Models Explainable - 5.1 Partial Dependence Plot (PDP) https://christophm.github.io/interpretable-ml-book/pdp.html

Nodes

• Breakpoint4 ×
• RowID3 ×
• Transpose3 ×
• Extract Table Spec2 ×
• Reference Column Filter2 ×
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Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Data Generation
• FeatureKNIME Javasnippet
• FeatureKNIME Machine Learning Interpretability Extension
• FeatureKNIME Math Expression (JEP)
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