Classification

By default, a Classification node automatically generates four models, one each using:

• Decision Tree

• General Linear Model

• Naive Bayes

• Support Vector Machine

All four models have the same input data, the same target, and the same case ID (if a case ID is specified).

If you do not want to build models using one of the default algorithms, then deselect that algorithm. You can still add models using the deselected algorithm to a Classification node.

By default, the node generates these test results for tuning:

• Performance Metrics

• Performance Matrix (Confusion Matrix)

• ROC Curve (Binary only)

• Lift and Profit. The default is set to the top 5 target values by frequency. You can edit the default setting. By default, the node does not generate selected metrics for Model tuning. You can select the metrics for Model tuning.

You can deselect any of the test results. For example, if you deselect Performance Matrix, a Performance Matrix is not generated by default.

By default, split data is used for test data. Forty percent of the data is used for testing, and the split data is created as a table. You can change the percentage used for testing and you can create the split data as a view instead of a table. If you create a table, then you can create it in parallel. You can use all of the build data for testing, or you can use a separate test source.