Summary

In a sense, this was a very theoretical chapter, as we introduced generic concepts with simple examples. We went over a few operations with a classic dataset. This, by now, is considered a very small problem. However, it has the advantage that we were able to plot it out and see what we were doing in detail. This is something that will be lost when we move on to problems with many dimensions and many thousands of examples. The intuitions we gained here will all still be valid.

Classification means generalizing from examples to build a model (that is, a rule that can automatically be applied to new, unclassified objects). It is one of the fundamental tools in machine learning, and we will see many more examples of this in forthcoming chapters.

We also learned that the training error is a misleading, over-optimistic estimate of how well the model does. We must, instead, evaluate it on testing data that was not used for training. In order to not waste too many examples in testing, a cross-validation schedule can get us the best of both worlds (at the cost of more computation).

We also had a look at the problem of feature engineering. Features are not something that is predefined for you, but choosing and designing features is an integral part of designing a machine-learning pipeline. In fact, it is often the area where you can get the most improvements in accuracy as better data beats fancier methods. The chapters on computer vision and text-based classification will provide examples for these specific settings.

In this chapter, we wrote all of our own code (except when we used NumPy, of course). This will not be the case for the next few chapters, but we needed to build up intuitions on simple cases to illustrate the basic concepts.

The next chapter looks at how to proceed when your data does not have predefined classes for classification.