  The area of Machine learning (ML) has seen exceptional growth in recent
years. Successful implementation of ML methods in various branches of physics
has led to new insights. These methods have been shown to classify phases in
condensed matter systems. Here we study the classification problem of phases in
a system of hard rigid rods on a square lattice around a continuous and a
discontinuous phase transition. On comparing a number of methods we find that
convolutional neural network (CNN) classifies the phases with the highest
accuracy when only snapshots are given as inputs. We study how the system size
affects the model performance. We further compare the performance of CNN in
classifying the phases around a continuous and a discontinuous phase
transition. Further, we show that one can even beat the accuracy of CNN with
simpler models by using physics-guided features. Lastly, we show that the
critical point in this system can be learned without any prior estimate by
using only the information of the ordered phase (as training set). Our study
reveals the ML techniques that have been successful in studying spin systems
can be easily adapted to more complex systems.
