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Mean Squared Error (MSE)

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

TL;DR. The average squared difference between predictions and true values.

Technical Definition

The average squared difference between predictions and true values.

How it works

MSE = (1/n) Σ (ŷᵢ − yᵢ)². Heavily penalizes large errors, making it sensitive to outliers. The default loss for regression and the basis of RMSE (its square root, in original units).

Related Concepts

  • Loss Function — A mathematical function that measures how far the model's predictions are from the actual values, guiding the learning process.
  • Regression — A supervised learning task that predicts a continuous numeric value.
  • Mean Absolute Error (MAE) — The average absolute difference between predictions and true values.