  Machine learning (ML) has been widely applied in high energy physics to help
the physical community in particle classification and data analysis. Here we
describe the application of machine learning to solve the problem of
classifying background and signal events for the DEAP-3600 dark matter search
experiment (SNOLAB, Canada). We apply Boosted Decision Trees (BDT) algorithm of
ML with improvements from Extra Trees and eXtra Gradient Boosting (XGBoost)
methods.
