  Machine learning algorithms are now being extensively used in our daily
lives, spanning across diverse industries as well as academia. In the field of
high energy physics (HEP), the most common and challenging task is separating a
rare signal from a much larger background. The boosted decision tree (BDT)
algorithm has been a cornerstone of the high energy physics for analyzing event
triggering, particle identification, jet tagging, object reconstruction, event
classification, and other related tasks for quite some time. This article
presents a comprehensive overview of research conducted by both HEP
experimental and phenomenological groups that utilize decision tree algorithms
in the context of the Standard Model and Supersymmetry (SUSY). We also
summarize the basic concept of machine learning and decision tree algorithm
along with the working principle of \texttt{Random Forest}, \texttt{AdaBoost}
and two gradient boosting frameworks, such as \texttt{XGBoost}, and
\texttt{LightGBM}. Using a case study of electroweakino productions at the high
luminosity LHC, we demonstrate how these algorithms lead to improvement in the
search sensitivity compared to traditional cut-based methods in both compressed
and non-compressed R-parity conserving SUSY scenarios. The effect of different
hyperparameters and their optimization, feature importance study using SHapley
values are also discussed in detail.
