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 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.