Hi,
While both SkLearn and TF-DF implement the classical Random Forest algorithm, there is some few differences in between the implementations. For this reason, it is expected for the results (both the model structure and model quality) not to be exactly the same (but still very close).
Following are some parameter values that should make sklearn’s RandomForestClassifier as close as possible to TF-DF’s Random Forest.
PS: Random Forest and Gradient Boosted Trees are different algorithms.
n_estimators = 300
max_depth = 16
criterion = "entropy"
min_samples_split = 5

In addition, if the problem is regressive, make sure to have:
max_features = 1./3

If your dataset contains categorical or categorical-set features, there are not equivalent parameters for sklearn as it does not support those type of features.
If the differences are larges, it would be very interesting for us to look at it.