  The decision making involved behind the mode choice is critical for
transportation planning. While statistical learning techniques like discrete
choice models have been used traditionally, machine learning (ML) models have
gained traction recently among the transportation planners due to their higher
predictive performance. However, the black box nature of ML models pose
significant interpretability challenges, limiting their practical application
in decision and policy making. This study utilised a dataset of $1350$
households belonging to low and low-middle income bracket in the city of
Bengaluru to investigate mode choice decision making behaviour using
Multinomial logit model and ML classifiers like decision trees, random forests,
extreme gradient boosting and support vector machines. In terms of accuracy,
random forest model performed the best ($0.788$ on training data and $0.605$ on
testing data) compared to all the other models. This research has adopted
modern interpretability techniques like feature importance and individual
conditional expectation plots to explain the decision making behaviour using ML
models. A higher travel costs significantly reduce the predicted probability of
bus usage compared to other modes (a $0.66\%$ and $0.34\%$ reduction using
Random Forests and XGBoost model for $10\%$ increase in travel cost). However,
reducing travel time by $10\%$ increases the preference for the metro ($0.16\%$
in Random Forests and 0.42% in XGBoost). This research augments the ongoing
research on mode choice analysis using machine learning techniques, which would
help in improving the understanding of the performance of these models with
real-world data in terms of both accuracy and interpretability.
