Fraud Detection: Random Forest Model Deployment
We read the trained model, as well as the new transaction and applies the model to classify it. We use a Rule Engine node to apply a threshold. In case a transaction is classified as fraudulent the workflow sends an email to notify of a fraud.
This workflow demonstrates how we can use the trained Random Forest Model on new data by performing the following steps:
1. Read the model and new data
2. Apply the model on the new transaction
We read the trained model, as well as the new transaction and applies the model to classify it. We use a Rule Engine node to apply a threshold. In case a transaction is classified as fraudulent the workflow sends an email to notify of a fraud.
This workflow demonstrates how we can use the trained Random Forest Model on new data by performing the following steps:
1. Read the model and new data
2. Apply the model on the new transaction