This workflow demonstrates the functionality of the Binary Classification Inspector node. It produces a complex view made of four different charts in order to compare, optimize and select predictions of different binary classifiers.
It is possible to compare a number of binary classifier machine learning models predicting the same target on the same test data using performance metrics and ROC curves. Here four machine learning models are used: Naive Bayes, Random Forest, Gradient Boosted Trees, Logistic Regression and Decision Tree.
By moving a threshold slider in the interactive view you can optimize a model by finding the best threshold given a performance metric of your choice.
It is possible to 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
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
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