Term tag: underfitting

Bias-Variance Tradeoff

Statistical bias, not fairness. Error splits into bias squared, variance and irreducible noise — derived, worked on numbers, and honest about where it breaks.

High Bias

Statistical bias, not AI fairness: the error a model class cannot escape even with infinite data — worked in closed form on a line fitted to a parabola.

Low Variance

Low variance means values sit close to their average — and for a model, that retraining it on fresh data barely moves its predictions. Worked on numbers.

Underfitting

Underfitting is when a model is too simple to capture the real pattern, so it does poorly on both training and new data — the mirror of overfitting.