Binary classification and deep neural networks
Binary classification problems, such as regression problems, are very common machine learning tasks. So much so that any book on deep learning wouldn't be complete without covering them. To be sure, we haven't really hit the sweet spot of deep neural networks quite yet, but we're well on our way. Before we get to the code, let's talk about the trade-offs you should consider when choosing a deep neural network to solve this kind of problem.
Benefits of deep neural networks
When compared to a more traditional classifier such as a logistic regression model, or even a tree-based model such as random forest or a gradient boosting machine, deep neural networks have a few nice advantages.
As with the regression we did in Chapter 2, Using Deep Learning to Solve Regression Problems, we don't need to select or screen features. In the problem that we have selected in this chapter, there are 178 input variables. Each input variable is a specific input from...
