juniorCost Function
What is Mean Squared Error?
Updated May 15, 2026
Short answer
MSE is the average of squared differences between predictions and actual values.
Deep explanation
MSE penalizes larger errors more heavily by squaring differences. It is widely used in regression problems due to its smooth gradient properties.
Real-world example
Used in predicting temperature forecasting models.
Common mistakes
- Not normalizing data before applying MSE.
Follow-up questions
- Why square errors instead of absolute values?
- When is MAE better than MSE?