Partial Dependence Plot with AutoML

This is an example for visualizing a partial dependence plot and an ICE curves plot in KNIME.
AutoML Component was used to select the best model for the given data, 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 Workflow Executor and AutoML Component.
- 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

External resources

• Verified Components
• Christoph Molnar - Interpretable Machine Learning - A Guide for Making Black Box Models Explainable - 5.1 Partial Dependence Plot (PDP)
• Data Source: UCI - Wine Quality Data Set