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By default a classification node automatically generates four models, one each using Decision Tree, General Linear Model, Naive Bayes, and 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 want to never build models using one of the algorithms by default, deselect that algorithm. A user will still be able to add models using the deselected algorithm to a classification node.
By default, the node generates these test results for tuning: Performance Metrics, Confusion Matrix, ROC Curve (binary only), Lift and Profit, and generates selected metrics for model tuning.
By default, split data is used for test data. The split is 40% and the split data is created as a table.
For detailed information about testing classification models, see Testing Classification Models and Tuning Classification Models.