Chapter 8. Privacy, Debugging, and Launching Your Products
Over the course of the last seven chapters we've developed a large toolbox of machine learning algorithms that we could use for machine learning problems in finance. To help round-off this toolbox, we're now going to look at what you can do if your algorithms don't work.
Machine learning models fail in the worst way: silently. In traditional software, a mistake usually leads to the program crashing, and while they're annoying for the user, they are helpful for the programmer. At least it's clear that the code failed, and often the developer will find an accompanying crash report that describes what went wrong. Yet as you go beyond this book and start developing your own models, you'll sometimes encounter machine learning code crashes too, which, for example, could be caused if the data that you fed into the algorithm had the wrong format or shape.
These issues can usually be debugged by carefully tracking which shape the data had...
