Can I tell a machine learning model that the dependent variable is normally distributed?
One way you could do this is by creating a custom objective function that penalizes predictions that are not normally distributed.
I am trying to set up a machine learning model predicting a continuous variable y on the basis of a feature vector (x1, x2, ..., xn). I know from elsewhere that y follows a normal distribution. Can I somehow specify this to the model and enhance its predictions this way? Is there a specific model that allows me to do this?
I have used linear models, k-nearest neighbour models and random forest models (in python). All of them give some predictions but I was wondering whether they can be outperformed by some model that would know the distribution of the predicted variable.