Machine Learning for Business Analytics, 2nd Edition

11 NEURAL NETS

In this chapter, we describe neural networks, a flexible data‐driven method that can be used for classification or prediction. Although considered a “blackbox” in terms of interpretability, neural nets have been highly successful in terms of predictive accuracy. We discuss the concepts of “nodes” and “layers” (input layers, output layers, and hidden layers) and how they connect to form the structure of a network. We then explain how a neural network is fitted to data using a numerical example. Because overfitting is a major danger with neural nets, we present a strategy for avoiding it. We describe the different parameters that a user must specify and explain the effect of each on the process. Finally, we move from a detailed description of a basic neural net to a more general discussion of the deeper and more complex neural nets that power deep learning.

Neural Nets in JMP: Basic neural network models can be fit using the standard version of JMP. However, JMP Pro is required for advanced functionality. Deep learning is not currently available in JMP or JMP Pro.

11.1 INTRODUCTION

Neural networks, also called artificial neural networks, are models for classification and prediction. The neural network is based on a model of biological activity in the brain, where neurons are interconnected and learn from experience. Neural networks mimic the way that human experts learn. The learning and memory properties of neural networks resemble the properties of human ...