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Martin Zinkevich's "Rule of Machine Learning" is a guideline often cited in the machine learning community to emphasize the importance of having a large, high-quality dataset. The rule states: 

"Your error on the training set should decrease every epoch. Your error on the validation set should decrease every epoch. Your error on the test set should decrease, or at least remain constant, for a while. When your test set error starts to increase, you are overfitting." 

This rule highlights the importance of monitoring model performance on both training and validation sets to ensure that the model is not overfitting the training data and can generalize well to unseen data. Overfitting occurs when a model learns to memorize the training data rather than capturing the underlying patterns, leading to poor performance on new, unseen data. 

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