Hardware Trojan (HT) is the most critical threat due to outsourcing of Integrated circuit designing phases. Therefore, a new technique is proposed that utilizes structural and SCOAP features to detect HT from the gate-level netlist using Light Gradient Boosting (Light GBM). Further, a model agnostic Shapley additive explanations (SHAP) is employed to identify each feature global and local impact on model prediction. Moreover, a quartile-based feature selection method is proposed, which uses SHAP to identify the optimal feature set by keeping low retraining rounds. Experimental results show that the proposed technique accurately detects always-on-Trojans and HT nets from Trust-Hub, DeTrust, and DeTest benchmarks.