This node requires the use of the Partial Dependence Pre-processing Component to sample the relevant data. This Component can be found on the EXAMPLES server. You can also navigate to the component via EXAMPLES > 00_Components > Model Interpretability > Partial Dependence Pre-processing.
For an example of how to use this node as well as the required Partial Dependence Pre-processing Component, please see this workflow on the KNIME Hub.
This node is able to visualize how the prediction at the output of a model reacts as a single column is changed in a defined range. Such visualization can help you interpret how any model is using a single column locally, that is visualizing the prediction change instance by instance, and globally, visualizing an overall behavior valid for the majority of the instances but not the outliers.
The Individual Conditional Expectation (ICE) visualizes the prediction change locally to a single instance in a simple line plot. You can visualize many ICE plots by having multiple lines in the same charts. Use the markers in the shape of dots to keep track the original column value and the original prediction each instance has. To visualize colors for different groups/category/clusters add a Color Manager node before this node.
The Partial Dependence Plot (PDP) visualizes the global average prediction change over a number of instances in a line plot. By default you can also visualize how widely the single instances predictions vary from the average by means of a colored area around the line plot.
The generated view is highly interactive and custom to your needs. Change the opacity and sizes of the visual elements to your needs from the node dialogue. From the view itself you can toggle between displaying both ICEs and PDP or just one of them.
ICE is useful to assess the exact prediction change an a single instance or on a small group of instances. When visualizing a global behavior of the model for many instances PDP can be useful, even if it could simplify too much the complexity behind such predictions.
The best way to use this node is in a Composite View by interactively filtering and selecting groups of similar instances.
The node supports custom CSS styling. You can simply put CSS rules into a single string and set it as a flow variable 'customCSS' in the node configuration dialog. You will find the list of available classes and their description on our documentation page.
General node options.
Partial Dependence and Individual Conditional Expectation Line Options.
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To use this node in KNIME, install the extension KNIME Machine Learning Interpretability Extension from the below update site following our NodePit Product and Node Installation Guide:
A zipped version of the software site can be downloaded here.
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