Deployment

The concept of deployment in predictive data mining refers to the application of a model for prediction or classification to new data. After a satisfactory model of set of models have been identified (trained) for a particular application, you usually want to deploy those models so that predictions or predicted classifications can quickly be obtained for new data. For example, a credit card company may want to deploy a trained model or set of models (e.g., neural networks, meta-learner) to quickly identify transactions that have a high probability of being fraudulent.

See also Data Mining.