# Should I divide my dataset in multiple prediction models?

Let's say I have an expertise in the domain knowledge of the dataset I am working on. I know that part of my dataset acts 100% differently than the rest.

Also, it is straightforward to check if a row belongs to one part of the dataset or the other with a couple of simple if-else.

Should I split my dataset upfront and create two different prediction models for the two parts of the dataset? Or should I keep one model and try to improve it?

Then when I want to use the model for predictions, I check the input values and see in which category it belongs and I call the appropriate model for it.

After all, you can actually count the following as a single model $$f(x) = \begin{cases} f_1(x) & ,x \in A \\ f_2(x) & ,x \in A^c \end{cases}$$