The bias-variance tradeoff represents the balance between a model's simplicity (bias) and its sensitivity to fluctuations in the training data (variance).
The bias-variance tradeoff represents the balance between a model's simplicity (bias) and its sensitivity to fluctuations in the training data (variance). Striking the right balance minimizes the total error and achieves the best performance.