Model building

In real-world problems, there are many constraints on learning and many ways to assess model performance on unseen data. Each modeling algorithm has its strengths and weaknesses when applied to a given problem or to a class of problems in a particular domain. This is articulated in the famous No Free Lunch Theorem (NFLT), which says—for the case of supervised learning—that averaged over all distributions of data, every classification algorithm performs about as well as any other, including one that always picks the same class! Application of NFLT to supervised learning and search and optimization can be found at http://www.no-free-lunch.org/.

In this section, we will discuss the most commonly used practical algorithms, giving the necessary details to answer questions such as what are the algorithm's inputs and outputs? How does it work? What are the advantages and limitations to consider while choosing the algorithm? For each model, we will include sample...