Introduction to Neural Networks

Neural networks are the preferred tool for many predictive data mining applications because of their power, flexibility, and ease of use. Predictive neural networks are particularly useful in applications where the underlying process is complex, such as:

• Forecasting consumer demand to streamline production and delivery costs.
• Predicting the probability of response to direct mail marketing to determine which households on a mailing list should be sent an offer.
• Scoring an applicant to determine the risk of extending credit to the applicant.
• Detecting fraudulent transactions in an insurance claims database.

Neural networks used in predictive applications, such as the multilayer perceptron (MLP) and radial basis function (RBF) networks, are supervised in the sense that the model-predicted results can be compared against known values of the target variables. Forecasting and Decision Trees allows you to fit MLP and RBF networks and save the resulting models for scoring.