Bias-variance decomposition explains model error. Bias (underfitting) means a model is too simple, failing to capture data patterns. Variance (overfitting) means a model is too complex, sensitive to training data, and generalizes poorly. The goal is to balance this trade-off to minimize total prediction error for optimal model performance. Reducing bias may increase variance, and vice-versa, requiring strategic adjustments like complexity changes or regularization.

Bias is systematic error; variance is prediction variability. High bias causes underfitting; high variance causes overfitting. The bias-variance trade-off means reducing one often increases the other, making optimal model selection a key challenge. Bias is systematic error; variance is prediction variability. High bias causes underfitting; high variance causes overfitting. The bias-variance trade-off means reducing one often increases the other, making optimal model selection