Parametric models have some really convenient attributes. Namely, they are fast to fit, don't require too much data, and can be very easily explained. In the case of linear and logistic regression, it's easy to look at coefficients and directly explain the impact of fluctuating one variable in either direction. In regulated industries, such as finance or insurance, parametric models tend to reign supreme, since they can be easily explained to regulators. Business partners tend to really rely on the insights that the coefficients produce. However, as is evident in what we've already seen so far, they tend to oversimplify. So, as an example, the logistic regression decision boundary that we looked at in the last section assumes a perfect linear boundary between two classes.
It is rare that the real world can be constrained into...
