Classification means generalizing from examples to build a model that assigns objects to a predefined class (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 look at many more examples of this in the forthcoming chapters.
In a way, this was a very abstract and theoretical chapter, as we introduced generic concepts with simple examples. We went over a few operations with the Iris dataset. This is a small dataset. However, it has the advantage that we were able to plot all the data 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 insight we gained here will still be valid.
You also learned that the training error is a misleading, over-optimistic estimate of how well...
