01_​Fraud_​Detection_​Row_​Input

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This workflow showcases how the Container Input (Row) and Container Output (Row) nodes can be used to create a REST API for a workflow that can then be deployed as inference workflow to KNIME Edge. The workflow can be called via REST and classifies a transaction as fraudulent or not fraudulent.

First, it applies some pre-processing by normalizing the data. Then, it reads in an already trained model and applies it to the input data. A custom prediction threshold is applied wit a Rule Engine node to get the final prediction (true for fraud, false for not fraud). Last, the output is post-processed and made ready for the response.

When deployed to KNIME Edge, a POST request with the following JSON body can be used for testing:

{
"V1": -1.65977253845451,
"V2": 1.28590703928839,
"V3": -3.34963337176211,
"V4": 2.05070755935895,
"Amount": 5.49
}


The response will be:

{
"Prediction": true
}

Nodes

• Model Reader2 ×
• Column Filter1 ×
• Column Rename1 ×
• Container Input (Row)1 ×
• Container Output (Row)1 ×
• Show all 8 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Ensemble Learning Wrappers
• FeatureKNIME Javasnippet
• FeatureKNIME JSON-Processing

Links

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