The flow trains a neural network and a decision tree to make this prediction of revenue increase.

After you run the flow to generate the model nuggets, you can test the results of the learning process. You do this by connecting the decision tree and network in series between the Type node and a new Analysis node, changing the Data Asset import node to point to goods2n.csv, and running the Analysis node. From the output of this node, in particular from the linear correlation between the predicted increase and the correct answer, you will find that the trained systems predict the increase in revenue with a high degree of success.

Further exploration might focus on the cases where the trained systems make relatively large errors. These could be identified by plotting the predicted increase in revenue against the actual increase. Outliers on this graph could be selected using the interactive graphics within SPSS Modeler, and from their properties, it might be possible to tune the data description or learning process to improve accuracy.