A recent post on the MSDN Forums raises the issue of reconstructing the Microsoft Neural Network and reproducing the prediction behavior based on the model content. The forum did not allow a very detailed reply and, as I believe this is an interesting topic, I will give it another try in this post. As an example, I use a small neural network which is trained on two inputs, X (having the states A and B) and Y (C and D) in order to predict a Z discrete variable, having the states E and F.

This post is a bit large, so here is what you will find:

* - a description of the topology of the Microsoft Neural Network, particularized for my example
* - a step-by-step description of the prediction phases
* - a set of DMX statements that exemplify how to extract network properties from a Microsoft Neural Network mining model content
* - a spreadsheet containing the sample data I used as well as a sheet which contains the model content and uses cell formulae to reproduce the calculations that lead to predictions (the sheet can be used to execute predictions for this network)

1). Finding models (functions) that describe and distinguish classes or concepts for future prediction
2). E.g., classify countries based on climate, or
classify cars based on gas mileage
3). Presentation: decision-tree, classification rule,
neural network
4). Prediction: Predict some unknown or missing
numerical values