Parameter Optimization (Table)

Adopt this component to optimize any number of parameters of any binary or multiclass classification model. The component optionally offers an interactive view to visualize the parameter search performed by the component.

This component requires the parameter ranges listed in a table, the training data partition and the workflow object with the learner and predictor nodes of the classification model you are optimizing.

The output of the component is a flow variable with the optimized parameter values. Connect the flow variable to the learner node and select those values in its flow variable panel to adopt the optimized parameters combination when training the final model.

Various settings are available: for example you can define the performance metric to be maximized (e.g. accuracy), or the optimization criteria,(e.g. brute-force/grid-search). Inside the component, cross validation takes place for each combination of parameters to avoid overfitting.

The former version of this component, “Parameter Optimization” (kni.me/c/A_91QC387NtvJ6g8), was hardcoded on Random Forest and two of its parameters. To understand how to use this new version on any classification model, data, and set of parameters (and without editing the workflow inside) inspect the example workflow referenced at the bottom of this page.

Input ports

1. Type: Table
   Parameters Table
   The parameter table should list one row for each parameter to be optimized and four columns in total: 2 string columns with the name of the parameter and its numerical type, either Number (integer) or Number (double); 3 numerical columns with start, stop and stepping of the parameter search.
2. Type: Table
   Training Data
   The training data with the target column to be classified and the feature columns to be learned.
3. Type: Workflow Port Object
   Workflow Object with Learner and Predictor
   The workflow object captured with KNIME Integrated Deployment Capture nodes. The workflow object should have 3 inputs: parameter combination flow variable, train partition table, validation partition table. The workflow segment captured in a workflow object should contain the learner and the predictor node. The learner node should have one one flow variable controlling for each of the parameters.

Output ports

1. Type: Flow Variable
   Best Parameters
   A flow variable that contains values with the best parameters found during the optimization process and the corresponding performance with the selected metric.