How You Should Validate Machine Learning Models

Large language models have already transformed the data science industry in a major way. One of the biggest advantages is the fact that for most applications, they can be used as is — we don’t have to train them ourselves. This requires us to reexamine some of the common assumptions about the whole machine learning process — many practitioners consider validation to be “part of the training”, which would suggest that it is no longer needed. We hope that the reader shuddered slightly at the suggestion of validation being obsolete — it most certainly is not. Here, we examine the very idea of model validation and testing. If you believe yourself to be perfectly fluent in the foundations of machine learning, you can skip this article. Otherwise, strap in — we’ve got some far-fetched scenarios for you to suspend your disbelief on. <a href="https://towardsdatascience.com/how-you-should-validate-machine-learning-models-f16e9f8a8f7a">Visit Now</a>