Neural Networks

Chapter Seven begins with a brief comparison of the functions of artificial neurons with real neurons. The need for input and output encoding is discussed, followed by the use of neural networks for estimation and prediction. A simple example of a neural network is presented, so that its topology and structure may be examined. A neural network consists of a layered, feed-forward, completely-connected network of artificial neurons, or nodes. The neural network is composed of two or more layers, though most networks consist of three layers, an input layer, a hidden layer, and an output layer. There may be more than one hidden layer, though most networks contain only one, which is sufficient for most purposes. Each connection between nodes has a weight associated with it. The combination function and the sigmoid activation function are examined, using a small data set for illustration. The back propagation algorithm is described, as a method for minimizing the sum of squared errors. The gradient descent method is explained and illustrated. Back propagation rules are discussed, as a method for distributing error responsibility throughout the network. A walk-through of the back propagation algorithm is performed, showing how the weights are adjusted, using a tiny sample data set. Termination criteria are discussed, as are benefits and drawbacks of using neural networks. The learning rate and the momentum term are motivated and illustrated. Sensitivity analysis is explained, for identifying the most influential attributes. Finally, an application of neural networks to a real-world large data set is carried out, using Insightful Miner. The resulting network topology and connection weights are investigated. The hands-on analysis problems include generating neural network models for the large churn data set.