Scikit's GBM assumptions on feature type
All features are continuous for gradient boosting (and practically all other estimators).
Tree-based models should be able to learn splits in categorical features that are encoded as "levels" (1, 2, 3) rather than dummy variables ([1, 0, 0], [0, 1, 0], [0, 0, 1]), but this requires deep trees instead of stumps and the exact ordering may still affect the outcome of learning.
Does scikit's GradientBoostingRegressor make any assumptions on the feature's type? Or does it treat all features as continuous? I'm asking because I have several features that are truly categorical that I have encoded using LabelEncoder().