Calculating model accuracy is a critical part of any machine learning project, yet many data science tools make it difficult or impossible to assess the true accuracy of a model. Often tools only validate the model selection itself, not what happens around the selection. Or worse, they don’t support tried and true techniques like cross-validation. All data scientists have been in a situation where you think a machine learning model will do a great job of predicting something, but once it’s in production, it doesn’t perform as well as expected. In the best case, this is only an annoying waste
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