Bias-Variance Tradeoff

This concept describes the dilemma in model building where reducing model bias, errors from incorrect assumptions in the learning algorithm, often increases variance, the model's sensitivity to small changes in the training data, and vice versa. Striking a balance is crucial for good generalization.