Icon

05_​Parameter_​Optimization_​(Table)_​Component_​on_​MLP

Parameter Optimization (Table) Component on MLP

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

The model used for parameter optimization in this case is Random Forest. 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.

A Table Creator is used to pass the parameter range (minimum, maximum, and step size) 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 output 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. Define in Table Creator one row for each parameter. 5 columns: Name, Datatype, Start, Stop, Step Size.
5. Name of the parameter in Table Creator should match the one stated in the Variable Creator node
6. Define parameters ranges, with start, stop and step size for each parameter with either "Number (double)" or Number "(integer)" datatype.
7. Configure Learner node within Capture to use the flow variables for 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



Parameter Optimization (Table) Component on a Generic Model This component can optimize a generic classification model and a generic set of numerical parameters. In this case the component performs a parameter optimization on neural network to optimize number of layers, neurons per layer and maximum iteration. Check the workflow description to find a step-by-step guide to adapt the workflow to your own classification model. Define ParametersParameters to be optimized should be the same naming andnumber in those two nodes Capture Model to be Optimized Retrain Model on Full Training Data 0: train1: testParameter to be optimizedCreate parametersfor workflow object ParameterOptimization (Table) CaptureWorkflow End CaptureWorkflow Start Partitioning Scorer RProp MLP Learner RProp MLP Learner Table Reader Variable Creator MultiLayerPerceptronPredictor MultiLayerPerceptronPredictor Table Reader Parameter Optimization (Table) Component on a Generic Model This component can optimize a generic classification model and a generic set of numerical parameters. In this case the component performs a parameter optimization on neural network to optimize number of layers, neurons per layer and maximum iteration. Check the workflow description to find a step-by-step guide to adapt the workflow to your own classification model. Define ParametersParameters to be optimized should be the same naming andnumber in those two nodes Capture Model to be Optimized Retrain Model on Full Training Data 0: train1: testParameter to be optimizedCreate parametersfor workflow object ParameterOptimization (Table) CaptureWorkflow End CaptureWorkflow Start Partitioning Scorer RProp MLP Learner RProp MLP Learner Table Reader Variable Creator MultiLayerPerceptronPredictor MultiLayerPerceptronPredictor Table Reader

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Keerthan Shetty
Created at: 2022-03-16
On NodePit since: 2024-08-14
Last update: 2024-09-16
Created with KNIME version: v4.6.0
Tags: parameter optimizationmachine learningData ScienceclassificationVerified ComponentMLPNeural Network

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!