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?


1 Answer 1


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.

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