This workflow reads in the creditcard.csv file and trains and evaluates a Random Forest model to classify transactions as either fraudulent or not. Notice the final Rule Engine node. This node classifies all transactions with fraud probability above 0.3 as fraudulent.
This workflow, the deployment workflow, reads the trained model, as well as the new transaction and applies the model to classify it. Like in the training workflow we use a Rule Engine node to apply the custom threshold. In case a transaction is classified as fraudulent the workflow sends an email to the fraud department.