  Natural gradient has been recently introduced to the field of boosting to
enable the generic probabilistic predication capability. Natural gradient
boosting shows promising performance improvements on small datasets due to
better training dynamics, but it suffers from slow training speed overhead
especially for large datasets. We present a replication study of NGBoost(Duan
et al., 2019) training that carefully examines the impacts of key
hyper-parameters under the circumstance of best-first decision tree learning.
We find that with the regularization of leaf number clipping, the performance
of NGBoost can be largely improved via a better choice of hyperparameters.
Experiments show that our approach significantly beats the state-of-the-art
performance on various kinds of datasets from the UCI Machine Learning
Repository while still has up to 4.85x speed up compared with the original
approach of NGBoost.
