What makes working on new machine learning (ML) use cases so exciting, and at times so frustrating, is ML’s lack of hard and fast rules. A few aspects of the model development process can be codified; for example, data should always be separated into strictly disjoint training and test sets to ensure that model performance isn’t attributable to overfitting. But at the heart of machine learning prototyping is a heavy dose of guesswork and intuition. What algorithm, or what representation of the data, will yield the most effective predictions? Once a candidate set of models is generated, evaluating them and…

Alexander Rich

Data Insights Engineer at Flatiron Health and cognitive psychology PhD

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