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TitleBest Practices in Machine Learning Applications

AbstractBuilding representative machine learning models that generalize well on future data requires careful consideration, both of the data at hand and of assumptions about the various available training algorithms. Data is rarely in an ideal form that enables algorithms to train effectively. Some algorithms are designed to account for important considerations such as variable selection and handling of missing values, whereas other algorithms require additional preprocessing of the data or appropriate tweaking of the algorithm options. Ultimate evaluation of a model’s quality requires appropriate selection and interpretation of an assessment criterion that is meaningful for the given problem. This paper discusses the most common mistakes that machine learning practitioners make and provides guidance for using these powerful algorithms to build effective models.