  Excellent ranking power along with well calibrated probability estimates are
needed in many classification tasks. In this paper, we introduce a technique,
Calibrated Boosting-Forest that captures both. This novel technique is an
ensemble of gradient boosting machines that can support both continuous and
binary labels. While offering superior ranking power over any individual
regression or classification model, Calibrated Boosting-Forest is able to
preserve well calibrated posterior probabilities. Along with these benefits, we
provide an alternative to the tedious step of tuning gradient boosting
machines. We demonstrate that tuning Calibrated Boosting-Forest can be reduced
to a simple hyper-parameter selection. We further establish that increasing
this hyper-parameter improves the ranking performance under a diminishing
return. We examine the effectiveness of Calibrated Boosting-Forest on
ligand-based virtual screening where both continuous and binary labels are
available and compare the performance of Calibrated Boosting-Forest with
logistic regression, gradient boosting machine and deep learning. Calibrated
Boosting-Forest achieved an approximately 48% improvement compared to a
state-of-art deep learning model. Moreover, it achieved around 95% improvement
on probability quality measurement compared to the best individual gradient
boosting machine. Calibrated Boosting-Forest offers a benchmark demonstration
that in the field of ligand-based virtual screening, deep learning is not the
universally dominant machine learning model and good calibrated probabilities
can better facilitate virtual screening process.
