Intermediate · Evaluation

Variance

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. The sensitivity of a model to small fluctuations in the training data, leading to inconsistent predictions on new data.

Technical Definition

The sensitivity of a model to small fluctuations in the training data, leading to inconsistent predictions on new data.

How it works

Variance refers to the amount that the estimate of the target function will change if different training data were used. High variance can cause a model to model the random noise in the training data, rather than the intended outputs (overfitting). It means the model performs well on training data but poorly on unseen data.

Related Concepts

• Overfitting — When a model learns noise and specific patterns in training data too well, causing it to perform poorly on new, unseen data.
• Generalization — A model's ability to perform well on new, unseen data — not just its training set.
• Bias (ethics/fairness) — Unfair prejudice or favoritism towards certain groups or things, which can influence data, system design, and user interactions.