Mean Squared Error (MSE)

#machine-learning #loss-in-ml #interview #evaluation

AKA MSE
AKA L2-loss
MSE is used in Regression problems
Sensitive to large errors for the squaring
Gradient decreases as loss gets closer to 0

Mean Squared Error Formula

$M S E = \frac{1}{n} \sum_{i = 1}^{n} (Y_{i} - {\hat{Y}}_{i})^{2}$

Pros:

Convex function, so one Global Minima
No Local Minima
If error is big, penalizes higher by squaring them
Good for predicting outliers as give more weight to them

Cons:

Bad at Handling Outliers
Same error for one big error and many small errors

Related Notes