University of Michigan - Ann Arbor

Training and test error behave diﬀerently. At the left end of the graph, training error and test error are both high. This is the underﬁtting regime. As we increase capacity, training error decreases, but the gap between training and generalization error increases. Eventually, the size of this gap outweighs the decrease in training error, and we enter the overﬁtting regime, where capacity is too large, above the optimal capacity.

Relationship between Capacity, Training and Test Errors, Bias-Variance Tradeoff in Machine learning

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

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Machine Learning Yearning (Deeplearning.ai)

When selecting $K$ for K-Fold CV, we should consider a bias-variance trade-off. K-Fold CV provides $K$ estimates of the model error. The mean of the errors indicates the model's bias. A lower mean value indicates a higher accuracy of the model. The model variance is calculated based on the standard deviation of the errors. A lower variance indicates a low variation in the model performance. Usually, it is difficult to reduce both model bias and variance.

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