Difference between predcting multiple outputs and single output with random forest I am trying to predict certain output features (6 in total) with random forest with the input features always being the same. I noticed that my random forest model always fits better when I am trying to predict each of these outputs with a separate model.
But when I try to predict all these outputs with just a single model the r2 score is worse than that of predicting these features with separate models.
I would interpret that my r2 score is getting averaged across all my output features. Is it right ?
And does it make any difference in predicting the outputs at once or with separate model even though the input features are the same for all the outputs that I am trying to predict.

Predicting all together and separately are different because different outputs may require different splits. If you predict together, you constraint that they share the tree.
