ABSTRACT
As we saw in Chapters 10 and 11, the machine learning community has already come up with many creative approaches to classication that can work in a wide variety of settings, so most of the time we can choose from what is already available and avoid inventing new classication methods. However, in many cases molecular biology experiments will yield new types of high-dimensional data with which we would like to train a classier and use it on new experimental data. In general, we don’t know in advance which classiers (let alone which parameters) to use in order to obtain good performance. erefore, it’s of critical importance for molecular biologists to know how to train classiers correctly and how to evaluate their performance.
