Mis on bias-variance trade-off?

Bias-variance trade-off is a fundamental concept in machine learning, describing the balance between two types of errors during model training:

• Bias — error arising from a model that is too simple to accurately describe the data (underfitting).
• Variance — error related to a model that is too sensitive to noise in the training data and generalizes poorly to new data (overfitting).

If the model is too simple, it will have high bias and low variance. If it is too complex, it will have low bias but high variance.

The goal is to find a balance where the model is sufficiently complex to capture patterns but not so complex that it fits the noise.

Example: training a regression with low-degree polynomials (linear regression), bias will be high and variance low; with high-degree polynomials — the opposite.