  Several pool-based active learning algorithms (AL) were employed to model
potential energy surfaces (PESs) with a minimum number of electronic structure
calculations. Theoretical and empirical results suggest that superior
strategies can be obtained by sampling molecular structures corresponding to
large uncertainties in their predictions while at the same time not deviating
much from the true distribution of the data. To model PESs in an AL framework
we propose to use a regression version of stochastic query by forest, a hybrid
method that samples points corresponding to large uncertainties while avoiding
collecting too many points from sparse regions of space. The algorithm is
implemented with decision trees that come with relatively small computational
costs. We empirically show that this algorithm requires around half the data to
converge to the same accuracy in comparison to the uncertainty-based
query-by-committee algorithm. Moreover, the algorithm is fully automatic and
does not require any prior knowledge of the PES. Simulations on a 6D PES of
\pyrrolew show that $\mathord{<}15\,000$ configurations are enough to build a
PES with a generalization error of 16~\invcm, whereas the final model with
around 50\,000 configurations has a generalization error of 11~\invcm.
