Home > Testing and Tuning Models > Testing Classification Models
Classification models are tested by comparing the predicted values to known target values in a set of test data. The historical data for a classification project is typically divided into two data sets: one for building the model and one for testing the model.
The test data must be compatible with the data used to build the model and must be prepared in the same way that the build data was prepared.
These are the ways to test classification and regression models:
The default is to split the input data into build data and test data.
You can use all of the build data as test data.
Attach two data source nodes to the build node. The first data source that you connect to the build node is the source of build data; the second node that you connect is the source of test data.
Unselect Perform Test in the Test section of Properties and use a Test Node.
The Test section defines how tests are done. The default is to test all classification and regression models.
By default, the test data is created by randomly splitting the build data into two subsets; 40% if the input data is used for the test set. You can also use all of the build data for testing.
Oracle Data Miner provides Test Metrics for Classification Models so that you can evaluate the model.
Classification Model Test and Results Viewers describes the classification test viewers.
After testing, you may wish to tune models, as described in Tuning Classification Models.