Checking For Train, Test, Split Success – Towards AI

The article by Adam Ross Nelson explores the importance of a successful train, test, and split in machine learning and data science to avoid over-fitting and other undesirable results. The author also discusses p-values and randomness in statistics, and provides code for evaluating the success of train test splits in practice. The full blog can be read for free on Medium.

source update: Checking For Train, Test, Split Success – Towards AI

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