Fraud Detection: Random Forest Model Training
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
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