Even though a model prediction can be made, there are times when it should taken with some skepticism. For example, if a new data point is substantially different from the training set, its predicted value may be suspect. In chemistry, it is not uncommon to create an "applicability domain" model that measures the amount of potential extrapolation from the training set. The applicable package will be used to demonstrate different method to measure how much a new data point is an extrapolation from the original data (if at all).
Max Kuhn
RStudio
This one is not like the others: Applicability Domain methods in R
Even though a model prediction can be made, there are times when it should taken with some skepticism. For example, if a new data point is substantially different from the training set, its predicted value may be suspect. In chemistry, it is not uncommon to create an "applicability domain" model that measures the amount of potential extrapolation from the training set. The applicable package will be used to demonstrate different method to measure how much a new data point is an extrapolation from the original data (if at all).