Parameter Optimization Loop on Logistic Regression Classification

This workflow shows an example of parameter optimization in a logistic regression model. In the logistic regression we optimize step size in (0,1] step =0.1 and variance in (0, 5] step = 0.1

The different parameters are output as flow variables by the Parameter Optimization Loop Start node. The parameter settings of the logistic regression algorithm are overwritten by the flow variables and trees with different settings are trained.

Since this is a binary classification we can use the ROC Curve node and create a flow variable with the AUC in each iteration. This is then fed into the Parameter Optimization Loop End node.
The end node compares the accuracies and supplies the best value in the first output. We use "Hill Climbing Strategy".

External resources

• ML Algorithms and the Art of Parameter Selection - KNIME Blog
• Parameter Optimization Video - KNIME TV - YouTube