This workflow shows an example of parameter optimization in a decision tree. In the decision tree we optimize the minimum number of records per node within a range [2,15] with step 1. The different parameters are output as flow variables by the Parameter Optimization Loop Start node. The parameter settings of the Decision Tree algorithm are overwritten by the flow variables and trees with different settings are trained. The Scorer node creates a flow variable with the actual accuracy 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.