Lastly, when you compare your algorithm to other algorithms, it is important that you measure the performance of your model in a similar way as done in the measurement of the other models. Otherwise, your comparison may be inaccurate. For example, F1 is generally considered to better measure the ability of the model to discriminate than e.g. hit ratio. Reading into the meaning of these measurements and choosing the appropriate one may be fundamental to your research.

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This is a small description of the picture: Lastly, when you compare your algorithm to other algorithms, it is important that you measure the performance of your model in a similar way as done in the measurement of the other models. Otherwise, your comparison may be inaccurate. For example, F1 is generally considered to better measure the ability of the model to discriminate than e.g. hit ratio. Reading into the meaning of these measurements and choosing the appropriate one may be fundamental to your research.