Mean Squared Error
A regression metric that calculates the average of the squared differences between predicted and actual values, with larger errors receiving proportionally greater penalisation.
In plain language
A way to measure how far off the AI's number predictions are. If it predicted house prices, MSE tells you the average of the squared differences between what it predicted and what the actual prices were.
Why this matters
Mean squared error is a standard metric for evaluating regression model performance in AI systems. Your implementation governance should ensure that evaluation metrics like MSE are tracked during model development and deployment, with clear thresholds defining acceptable performance levels for production systems.
Relevance
ImplementationMSE is a standard performance metric used to validate that regression models meet accuracy requirements before deployment.
Related terms
Putting mean squared error into practice in your organisation?
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