Parameter Optimization (Table) Component with Range Sliders on Multilayer Perceptron

This workflow shows an example for the "Parameter Optimization (Table)" component (kni.me/c/dIpKMJbiO-3019eb) with range filters.

The model used for parameter optimization in this case is Multilayer Perceptron. The Learner and Predictor nodes are captured with Capture Workflow nodes, exported in the black Workflow Object Port and adopted in the component via a Workflow Executor node. Thus, we can use this component with any classification model without making any changes to the component.
The different combination of parameters is passed using a Table Reader, Parameter Ranges component is used to set the parameter range (minimum, maximum, and step size) of all parameters to be used for optimization. A Variable Creator is used to send the initial set of parameters to the capture node.
The output of the component is a flow variable with the best of parameters. This outputs flow variable automatically configures another Learner node to train the final model.

STEPS TO FOLLOW TO ADAPT WORKFLOW ON YOUR OWN CLASSIFICATION MODEL:

1. Import your training data with a Reader node
2. Replace the Learner and Predictor nodes with the desired ones with the Capture nodes.
3. Define suitable parameters in the Variable Creator nodes with precise names (they will display in interactive view).
4. Import different combination of parameters using a table reader node, the name of the columns should match the one stated in the Variable Creator node.
5. Open "Parameter Ranges" component and setup the same variable names in the Interactive Range Slider Filter Widget, Integer/Double Widget and Table Creator, set the filter to pass only the required parameters.
6. Execute the Parameter Ranges component and set the minimum, maximum, and step size of each parameter.
7. Configure Learner node within Capture to use the flow variables from Variable Creator node (Flow Variable panel).
8. Configure the component with the required options.
9. Execute the component on the training set and "Open Interactive View" to inspect the results.
10. Train the model with the best parameter combination with another Learner node, using the flow variable output from the component.
11. Test the model on the test set with another Predictor node.

External resources

• KNIME Verified Components - knime.com
• ML Algorithms and the Art of Parameter Selection - KNIME Blog