Learning and Testing

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

Once you have executed the model nodes and generated the actual models, 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 input (data) file to GOODS2n, and executing 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 could 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.