Is the process of assessing how well your mining models perform against real data. It is important that you validate your mining models by understanding their quality and characteristics before you deploy them into a production environment.

There are many approaches for assessing the quality and characteristics of a data mining model.

• Use various measures of statistical validity to determine whether there are problems in the data or in the model.
• Separate the data into training and testing sets to test the accuracy of predictions.
• Ask business experts to review the results of the data mining model to determine whether the discovered patterns have meaning in the targeted business scenario

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All of these methods are useful in data mining methodology and are used iteratively as you create, test, and refine models to answer a specific problem. No single comprehensive rule can tell you when a model is good enough, or when you have enough data.