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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

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What bias-variance tradeoff do lower-order polynomials (low model complexity) have?

What bias-variance tradeoff do higher-order polynomials (high model complexity) have?

When selecting $$K$$ for $$K$$-Fold Cross-Validation, we must consider the bias-variance tradeoff. $$K$$-Fold Cross-Validation provides $$K$$ estimates of the model error. The mean of these errors indicates the model's bias, where a lower mean value implies higher accuracy. The model variance is determined by the standard deviation of these errors, where a lower variance indicates less fluctuation in model performance. Usually, it is difficult to reduce both model bias and variance simultaneously.

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