03b_Call_the_prediction_workflow_from_a_data_app
Calling a prediction workflow via a data app. Example scenario: The bank provides their employees with the credit scoring data app to score the applicants […]
03b_Call_the_prediction_workflow_from_a_data_app
Calling a prediction workflow via a data app. Example scenario: The bank provides their employees with the credit scoring data app to score the applicants […]
03.3 Call the prediction workflow from a data app
Calling a prediction workflow via a data app. Example scenario: The bank provides their employees with the credit scoring data app to score the applicants […]
Nashville_meetup_demo
The workflow describes a simple use case how Neo4j Graph Data Science and Knime algorithms can be used together.
prototype
There has been no description set for this workflow's metadata.
Nashville_meetup_demo_batch_mode
The workflow describes a simple use case how Neo4j Graph Data Science and Knime algorithms can be used together.
Nashville_meetup_demo
The workflow describes a simple use case how Neo4j Graph Data Science and Knime algorithms can be used together.
Training_Workflow1
There has been no description set for this workflow's metadata.
01_Train_and_Explain_Keras_Network_with_Counterfactuals
This application is a simple example of using Conterfactual Explanations (Python) Component to identify the counterfactual instances for a Binary […]
Python Transform
This Component transforms values of the user selected columns to normalized values by standardizing them across their mean values. The Component uses the […]