I just started reading Introduction to Statistical Learning with R and I am currently trying to work through the exercises.
One of the questions is "What are the advantages and disadvantages of a very flexible (versus a less flexible) approach for regression or classification?"
when looking through online answers to check my own, I got right the advantages however I found this for the disadvantages:
The disadvantages for a very flexible approach for regression or classification are requires estimating a greater number of parameters, follow the noise too closely (overfit), increasing variance.
Though I understand the noise and variance disadvantages, I don't see the connection with parameters? How does the relation between parameters and Flexible models differ from the relation between parameters and non-flexible models?