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Lesson 5: Testing Models (Basic Data Mining Tutorial)

Now that you have processed the model by using the targeted mailing scenario training set, you will test your models against the testing set. Because the data in the testing set already contains known values for bike buying, it is easy to determine whether the model's predictions are correct. The model that performs the best will be used by the Adventure Works Cycles marketing department to identify the customers for their targeted mailing campaign.

In this lesson you will first test your models by making predictions against the testing set. Next, you will test your models on a filtered subset of the data. Analysis Services provides a variety of methods to determine the accuracy of mining models. In this lesson we will take a look at a lift chart.

Validation is an important step in the data mining process. Knowing how well your targeted mailing mining models perform against real data is important before you deploy the models into a production environment. For more information about how model validation fits into the larger data mining process, see Data Mining Concepts (Analysis Services - Data Mining).

This lesson contains the following tasks:

Testing Accuracy with Lift Charts (Basic Data Mining Tutorial)

Testing a Filtered Model (Basic Data Mining Tutorial)

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