Recently, I have started reading a book "Introduction to statistical Learning", which had good introduction for model accuracy assessing. This post contains excerpts of the chapter:
Often we take different statistical approaches to build a solution for a data analytical problem. Why is it necessary to introduce so many different approaches, rather than a single best method? The answer is: in Statistics no single method dominates all other methods over all possible datasets. One statistical method may work well with a specific dataset and some other method may work better on a similar but different dataset. So it is important to decide for a particular dataset which method produces best results.
Often we take different statistical approaches to build a solution for a data analytical problem. Why is it necessary to introduce so many different approaches, rather than a single best method? The answer is: in Statistics no single method dominates all other methods over all possible datasets. One statistical method may work well with a specific dataset and some other method may work better on a similar but different dataset. So it is important to decide for a particular dataset which method produces best results.
