The 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 Rule Engine node classifies all transactions with fraud probability above 0.3 as fraudulent. We apply a threshold of 0.3 to the probability of being a fraudulent transaction (default is 0.5). Adopting a lower threshold makes the algorithm more responsive in classifying frauds. You can evaluate the results by opening 'Evaluation' component view. After training, the model is saved for deployment. In our case we use Random Forest Learner, but we can use any other Supervised model.
This workflow demonstrates how we can train the model on the provided data:
1. Read training data
2. Train the Random Forest Model
3. Evaluate model results
4. Save trained model for deployment
URL: Kaggle Dataset https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
Download WorkflowDeploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.
Try NodePit Runner!Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com, follow @NodePit on Twitter or botsin.space/@nodepit on Mastodon.
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.