Binary Classification Inspector Example
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 three machine learning models are used: Bayesian, RandomForest, and XGBoost 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
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 three machine learning models are used: Bayesian, RandomForest, and XGBoost 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