This node produces a complex view made of four different charts in order to compare, optimize and select predictions of different binary classifiers:
- Compare a number of binary classifier machine learning models predicting the same target on the same test data using performance metrics and ROC curves
- Optimize a model by finding the best threshold given a performance metric of your choice
- Interactively select a given type of predictions (e.g. true positives) of one of the models and export them at the output of the node
The user journey when using this view follows these steps:
- Compare model AUC to select the best model via the Model's Statistics bar chart and the Model's ROC Curves chart in the top panel;
- Change the threshold from it's initial value either manually, via the Threshold Slider , or automatically maximizing one of the available function (e.g. F-Measure) via the Model Tab Dropdown.
- Look back at the top panel to see how the new threshold impacts the model when compared with the other models.
- Inspect the Confusion Matrix in the bottom panel to assess the gravity of the misclassification, give the associated probability confidence of the model on the Classification Distribution chart.
- Combine this view with other KNIME views in a Component to interactively visualize different types of visualizations (e.g. false positives) with interactive selection events.
- After interaction use the "Apply" features to export the new threshold and selected model from the node as flow variables. This will also export: A) selected model predictions B) selection of the confusion matrix cell C) selected model performance statistics.
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.