When standing up a model to use in the real world there is a long list of considerations to take into account, from checking the integrity of the training data to understanding the biases and assumptions in different modeling algorithms to selecting the best metrics for evaluating the model. All of these considerations (and many more) are vital steps to creating a quality model. For each step there are a vast number of solutions, all of which are better or worse depending on the data at hand. Having an intimate understanding of the data (specifically any biases in the training…

Alec Delany

Engineer turned Data Scientist.

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