I agree that we could have a generic sparse matrix to dense array transformer, however I would also be +1 for having the random tree embedding support dense output by using a parameter named `dense_output` instead of `return_dense` to be consistent with the transformers in `sklearn.random_projection`:

  https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/random_projection.py