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

• 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:

• By splitting the input data into build data and test data. This is the default. The test data is created by randomly splitting the build data into two subsets. 40 percent of the input data is used for test data.

• By using all the build data as test data.

• By attaching two Data Source nodes to the build node.

  • The first data source that you connect to the build node is the source of the build data.

  • The second node that you connect is the source of the test data.

• By deselecting Perform Test in the Test section of the Properties pane and using a Test node. The Test section define how tests are done. By default, all Classification and Regression models are tested.

Oracle Data Miner provides test metrics for Classification models so that you can evaluate the model.

After testing, you can tune the models.