Selecting the best performing machine learning model with optimal hyperparameters can sometimes still end up with a poorer performance once in production. This phenomenon might be the result of tuning the model and evaluating its performance on the same sets of train and test data. So, validating your model more rigorously can be key to a successful outcome.
Originally from KDnuggets https://ift.tt/30xpawc
source https://365datascience.weebly.com/the-best-data-science-blog-2020/key-machine-learning-technique-nested-cross-validation-why-and-how-with-python-code
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