  Top tagging has emerged to be a fast-evolving subject due to the top quark's
significant role in probing physics beyond the standard model. For the
reconstruction of top jets, machine learning models have shown a significant
improvement in the tagging and classification performance compared to the
previous methods. In this work, we build top taggers using $N$-Subjettiness
ratios and several observables of the Energy Correlation functions as input
features to train the eXtreme Gradient BOOSTed decision tree (XGBOOST). It is
observed that the performance of the taggers depends on how well the top jets
are matched to their truth-level partons. Furthermore, we use SHapley Additive
exPlanation (SHAP) framework to calculate the feature importance of the trained
models. It helps us to estimate how much each feature of the data contributed
to the model's prediction and what regions are of more importance for each
input variable. Finally, we combine all the tagger variables to form a hybrid
tagger and interpret the results using the Shapley values.
