Bias-Variance Tradeoff

The bias-variance tradeoff is the fundamental problem in machine learning where a model's error is decomposed into bias and variance components. Bias represents the error from erroneous assumptions in the model, while variance represents the error from sensitivity to small fluctuations in the training set.

A model with high bias is too simple and misses the pattern, while a model with high variance is too complex and fits the noise. In options pricing and crypto derivatives, finding the sweet spot between these two is critical for accurate modeling.

The goal is to minimize the total error by balancing simplicity and complexity. It is the core challenge of every quantitative researcher.