The Mean Squared Error (MSE) is a loss function that is commonly used for regression models.
The equation for the MSE is as follows:
In this equation:
- N represents the total number of data points.
- $f(x_n;w)$ represents the model's prediction based on the input $x_n$ and trainable parameters $w$.
- $y_n$ represents the ground truth.
The MSE is essentially summing up the square of the difference between the model's prediction and the ground truth.