Big Data

The Bias-Variance Tradeoff describes the balancing act in machine learning between underfitting and overfitting. High bias models oversimplify, missing patterns, resulting in poor training and testing accuracy. High variance models are overly complex, capturing noise as if it were signal, leading to poor generalization. Finding the right balance minimizes total error, achieving optimal predictive performance on unseen data.