In the previous chapter, we created a workflow to generate a grid. That must have looked pointless at that time, but now, we will move a bit forward and show an application. The GenerateGridForLogisticRegression.zip file contains the workflow demonstrating this idea with the iris dataset.
In this workflow, we use a setup very similar to the Generate Grid workflow till the preprocessing meta node, but in this case, we use the average of minimum and maximum values instead of creating NaN values when we generate a grid with a single value in that dimension. (This will be important when we apply the model.) We also modified the grid parameters to be compatible with the iris dataset. In the lower region of the workflow, we load the iris dataset from http://archive.ics.uci.edu/ml/datasets/Iris, so we can create a logistic regression model with the Logistic Regression (Learner) node (it uses all numeric columns).
We would like to apply this model to both the data and the grid....
