I have a database of 200 trees grown in different conditions, since it's very expensive to get more points I want to predict with a machine learning model the growth potential of tree seedlings:
- X variables: type of soil, exposure, species, etc.
- Y variable: growth potential
to help decision-making on which of two species should I plant on a new location:
I need to compare the predictions returned by my model, given that
Y are both subject to uncertainty (I have a known error in the measurement of my X and Y variables (potentially a different error attached to each data point)). For instance, if
Y(B)=59, I would like to compute the probability that species A will indeed give a better growth than B, the probability that it will give the inverse outcome, or a similar outcome (maybe the distribution of the difference of prediction?)
(What I have done so far: I thought of doing some bootstrap on the points, compute again the model, and see in how many cases
Y(A)>Y(B). But still, this doesn't integrate the uncertainty of the X variables so the result is far from correct (too confident imo). I also tried to plot the differences between all couples of points, but I don't know if this is valid. Eventually, I would prefer if the method does not involve any refitting of the model because for operational use it won't be possible.)
Any help on a method would be appreciated. Thanks!
EDIT precisions on the model type and why I don't want to refit it. The model can be either of 2 models: statistical model (e.g.: ML) or physical model based on simulation and very heavy to compute. The second one can be obviously subject to bias because it's a simulation. Let's assume I can have a measure of uncertainty for each X point, at least.