ML model trained on 2 sets of features - both sets deliver good OOS accuracy on their own, but combined result is same/worse than alone. Why? I'm using RandomForest to predict some financial data (doing binary prediction). I have 4500 rows in my training data and 1500 in my test data (I'm actually using nested cross validation on a larger data set but let's keep it simple).
Feature Set 1 has 7 variables, and it delivers OOS 69% accuracy (baseline accuracy is 59%).
Feature Set 2 has 36 variables, and it delivers 66% accuracy on same OOS data.
The two feature sets are very uncorrelated. When I put both sets together and train an RF model, the result is around 68% accuracy. I'm training 300 trees, and increasing trees on combined feature set doesn't improve results.
Why does this happen? Both feature sets seem to provide orthogonal predictive value - why does using them simultaneously not yield better performance than each on their own? Is there any solution?
EDIT: I have found that even though the variables in the two feature sets are quite different, the predictions generated by models trained on the two sets are pretty similar. I think this might be the reason why I'm seeing no improvement when combined?

As I mentioned in my EDIT, I think I have found the answer. Even though the features in the two sets seem very different, the predictions generated by the two sets independently are very similar - so combining them didn't improve the accuracy.
