Browsing the model

  1. Run the stream to create the model nuggets, which are added to the stream and to the Models palette in the upper-right corner. To view their details, double-click on any of the model nuggets in the stream.

    The model nugget Model tab is split into two panes. The left pane contains a network graph of nodes that displays the relationship between the target and its most important predictors, as well as the relationship between the predictors.

    The right pane shows either Predictor Importance, which indicates the relative importance of each predictor in estimating the model, or Conditional Probabilities, which contains the conditional probability value for each node value and each combination of values in its parent nodes.

    Figure 1. Viewing a Tree Augmented Naïve Bayes model
    Viewing a Tree Augmented Naïve Bayes model
  2. Connect the TAN model nugget to the Markov nugget (choose Replace on the warning dialog).
  3. Connect the Markov nugget to the Markov-FS nugget (choose Replace on the warning dialog).
  4. Align the three nuggets with the Select node for ease of viewing.
    Figure 2. Aligning the nuggets in the stream
    Aligning the nuggets in the stream
  5. To rename the model outputs for clarity on the Evaluation graph that you'll be creating, attach a Filter node to the Markov-FS model nugget.
  6. In the right Field column, rename $B-default as TAN, $B1-default as Markov, and $B2-default as Markov-FS.
    Figure 3. Rename model field names
    Rename model field names

    To compare the models' predicted accuracy, you can build a gains chart.

  7. Attach an Evaluation graph node to the Filter node and execute the graph node using its default settings.

    The graph shows that each model type produces similar results; however, the Markov model is slightly better.

    Figure 4. Evaluating model accuracy
    Evaluating model accuracy

    To check how well each model predicts, you could use an Analysis node instead of the Evaluation graph. This shows the accuracy in terms of percentage for both correct and incorrect predictions.

  8. Attach an Analysis node to the Filter node and execute the Analysis node using its default settings.

As with the Evaluation graph, this shows that the Markov model is slightly better at predicting correctly; however, the Markov-FS model is only a few percentage points behind the Markov model. This may mean it would be better to use the Markov-FS model since it uses fewer inputs to calculate its results, thereby saving on data collection and entry time and processing time.

Figure 5. Analyzing model accuracy
Analyzing model accuracy

Explanations of the mathematical foundations of the modeling methods used in IBM® SPSS® Modeler are listed in the IBM SPSS Modeler Algorithms Guide, available from the \Documentation directory of the installation disk.

Note also that these results are based on the training data only. To assess how well the model generalizes to other data in the real world, you would use a Partition node to hold out a subset of records for purposes of testing and validation.