Bias-Variance Tradeoff

High bias can cause underfitting, while high variance can cause a model to perform well on training data but poorly on new examples. Model selection, regularization, data quality, and validation are used to find an appropriate balance. Understanding the concept helps practitioners choose training methods, evaluate model quality, and reduce errors when predictions are used in production systems.