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A Comparative Study for the Selection of Machine Learning Algorithms based on Descriptive Parameters

In this paper, we present a new cheat sheet based approach to select an adequate machine learning algorithm. However, we extend existing cheat sheet approaches at two ends. We incorporate two different perspectives towards the machine learning problem while simultaneously increasing the number of parameters decisively. For each family of machine learning algorithms (e.g. regression, classification, clustering, and association learning) we identify individual parameters that describe the machine learning problem accurately. We arrange those parameters in a table and assess known machine learning algorithms in such a table. Our cheat sheet is implemented as a web application based on the information of the presented tables.