I have a regression use case where I am supposed to estimate a value based on 3-4 features. Using random forest, I was able to get ~20% error. However, I have a constraint now. I can overestimate but not underestimate. So, at the cost of improving the error I am allowed to overestimate. What is the right approach of handling this constraint? Is it okay to just go with an approach like 1.2x the estimation provided by the model?
Always overestimate
regression
Use an asymmetric loss function with a cliff at your margin; e.g.,$\mathcal L (x) \equiv \begin{cases}x^2 && x > 0\\ c x^2 && -m < x < 0 \\d x^{2p} && x < -m\end{cases}$where $c>1$, $p>1$, $d\equiv cm^{2-2p}$. The idea is to encode your constraints in your loss function.