During the development of a model, the faster one can iterate, the better. That means getting your training time down to a manageable level, albeit at the cost of some accuracy. With large training sets, training time for random forests can be an impediment to rapid iteration. @jph00 showed me this awesome trick that convinces scikit-learn's RF implementation to use a subsample of the full training data set when selecting split variables and values at each node:

def set_RF_sample_size(n):
    forest._generate_sample_indices = \
        (lambda rs, n_samples: forest.check_random_state(rs).randint(0, n_samples,n))

def reset_RF_sample_size():
    forest._generate_sample_indices = (lambda rs, n_samples:
        forest.check_random_state(rs).randint(0, n_samples, n_samples))

We both find that splitting using say 20,000 samples rather than the full 400,000 samples gives nearly the same results but with much faster training time. Naturally it requires a bit of experimentation for each data set to find a suitable subsample size, but in general we find that feature engineering and other preparation work is totally fine using a subsample. We then reset the sample size for the final model.

We appreciate your consideration of this feature request!