Loss Function

It is described as a mathematical function that quantifies the difference between the predicted and actual outcomes in machine learning algorithms.

$\text{MSE}=\frac{1}{n}{\sum }_{i=1}^n(y_i-{\hat{y}}_i{)}^2$

Variable breakdown:

• $n$ = total number of data points
• $y_i$ = actual / target value for the $i$-th instance
• ${\hat{y}}_i$ = predicted value generated by the model for the $i$-th instance

Questions

What is predicted ?

What is actual outcomes?