ENH: Added Random Forest feature importance based on out of bag data

This follows Breiman's proposed way of computing feature importances
on the out-of-bag samples for each tree (see section 10 in
Breiman, Random Forests, Machine Learning, 2001).

For the j-th feature, each tree outputs two predictions.
The first one is based on the original data and the second one
on data where the the j-th feature is randomly permuted.
Both predictions are compared to the true value and the difference
in performance is used as an importance measure.
In the case of regression, the R^2 score is used to asses performance
and accuracy in the case of classification.