MSE & MAE for Regression
Penalizing distance from the target.
Regression Needs Its Own Loss
When your model predicts a number like a price or temperature, you need a regression loss that measures how far each guess lands from the true value. 📏
Meet Mean Squared Error
MSE squares every prediction error, then averages them. Squaring keeps all errors positive and punishes big misses much harder than small ones.
All lessons in this course
- MSE & MAE for Regression
- Binary Cross-Entropy with Logits
- Cross-Entropy for Multiclass
- Class Weights for Imbalanced Data