I'm working on a problem where I need to fit a regression on solubility data from a collection of molecules. The response variable (solubility) displays a bimodal distribution, suggesting there are different populations in my data:
After training a baseline random forest model, I plotted the regression line and the bimodal nature of the target variable is pretty visible:
However, when I inspected the residuals I noticed the were centered around 0 but still showing the bimodal distribution
I'm not sure what would be a sound approach here (its my first time seeing this kind of distribution in this particular type of data).
I read in here that regressions don't make assumptions about the distribution of the target variable. Does that apply for all algorithms (e.g. PLS, random forest etc)?
I was following this kernel and it seems the author was trying to separate the populations. Is this a possible solution if a regression is required? My intuition is that we could use the classifier to predict from which population a sample comes from and then use the equivalent regression model to make predictions.
Is the bimodal pattern in my residual plot worrying or I should only be concerned if they are centered around zero?