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A detailed case study of $\gamma$-hadron segregation for a ground basedatmospheric Cherenkov telescope is presented. We have evaluated and comparedvarious supervised machine learning methods such as the Random Forest method,Artificial Neural Network, Linear Discriminant method, Naive BayesClassifiers,Support Vector Machines as well as the conventional dynamicsupercut method by simulating triggering events with the Monte Carlo method andapplied the results to a Cherenkov telescope. It is demonstrated that theRandom Forest method is the most sensitive machine learning method for$\gamma$-hadron segregation.

Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads