Home > Model Nodes > Association Node > Classification Node > Default Behavior for Classi...
For a binary target, the Classification Node builds models using the following four algorithms:
If the target is binary, all four algorithms are used. If the target is not binary, GLM is not built by default; you can explicitly add a GLM model to the node.
The models have the same build data and the same target.
Note: I f do not want to create a particular model, delete the model from the list of models. The blue check mark to the left of the model name selects models to be used in subsequent nodes. It does not select models to build. |
By default, the models are all tested. The test data is created by randomly splitting the build data into a build data set and a test data set. The default ratio for the split is 60% build and 40% test.
You can also connect both a build data source and a test data source to the build node.
You can also test classification models using a Test Node along with separate test data.
To interpret test results, s.ee Classification Model Test Viewer
After you have tested a classification, you can tune each model as described in Tuning Classification Models.
Case ID is optional.