Data Mining for Business Analytics

CHAPTER 11 Neural Nets

In this chapter, we describe neural networks, a flexible data-driven (albeit blackbox) method that can be used for classification, prediction and feature extraction, and is the basis for deep learning—a powerful technique that lies behind many artificial intelligence applications like image and voice recognition. We discuss the concepts of “nodes” and “layers” (input layers, output layers, and hidden layers) and how they connect to form the structure of the 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.

Python

In this chapter, we will use pandas for data handling and scikit-learn for the models. We will also make use of the utility functions from the Python Utilities Functions Appendix. Use the following import statements for the Python code in this chapter.

 import required functionality for this chapter

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier from dmba import classificationSummary ...