  The empirical classification of gamma-ray bursts (GRBs) into long and short
GRBs based on their durations is already firmly established. This empirical
classification is generally linked to the physical classification of GRBs
originating from compact binary mergers and GRBs originating from massive star
collapses, or Type I and II GRBs, with the majority of short GRBs belonging to
Type I and the majority of long GRBs belonging to Type II. However, there is a
significant overlap in the duration distributions of long and short GRBs.
Furthermore, some intermingled GRBs, i.e., short-duration Type II and
long-duration Type I GRBs, have been reported. A multi-parameter classification
scheme of GRBs is evidently needed. In this paper, we seek to build such a
classification scheme with supervised machine learning methods, chiefly
XGBoost. We utilize the GRB Big Table and Greiner's GRB catalog and divide the
input features into three subgroups: prompt emission, afterglow, and host
galaxy. We find that the prompt emission subgroup performs the best in
distinguishing between Type I and II GRBs. We also find the most important
distinguishing feature in prompt emission to be T_{90}, hardness ratio, and
fluence. After building the machine learning model, we apply it to the
currently unclassified GRBs to predict their probabilities of being either GRB
class, and we assign the most probable class of each GRB to be its possible
physical class.
