The　driving　test　is　the　only　way　to　verify　that　learner　drivers　have　acquired　the　competencies　stipulated　in　the　national　curriculum.　Therefore,　exploring　the　key　factors　that　influence　the　outcome　of　the　driving　test　is　of　particular　importance　in　assisting　learner　drivers　to　gain　solid　behind-the-wheel　skills.　Interpretable　machine　learning　(ML)　is　employed　to　analyze　the　probability　of　learner　drivers＇　passing　the　driving　skills　test　(called　the　Subject　2　test　in　China)　using　a　data　set　comprising　personal　characteristics,　training　mode,　frequency　of　driving　errors,　deducted　points,　percentage　of　qualified　training　times,　and　score　of　constructed　graphs　related　to　driving　behaviors.　The　data　are　collected　from　a　driving　school　in　China.　A　prediction　model　of　the　Subject　2　test　outcome　is　constructed　by　adapting　the　Light　Gradient　Boosting　Machine　(LightGBM)　ML　method.　Furthermore,　the　SHapley　Additive　exPlanation　(SHAP)　is　employed　to　explore　the　relationships　between　key　influencing　factors　and　the　aforementioned　outcome.　The　results　indicate　that　the　LightGBM　predicts　the　outcome　of　the　Subject　2　test　effectively.　The　deducted　points　in　the　real　car　training　(DP-RC)　and　the　frequency　of　driving　errors　in　virtual　reality　(VR)　training　(FE-VR)　have　a　significant　impact　on　the　probability　of　passing　the　Subject　2　test.