Squared error is the square of the difference between a predicted value and the true value. Where:
- is the true value
- is the predicted value
Definition
It measures how far of a prediction is, but by squaring the difference:
- Makes all errors positive
- Penalizes larger errors more heavily
Example
If the true value is 10 and the prediction is 7: So the squared error = 9
It is the basic building block of Mean Squared Error (MSE), where you average squared errors over many predictions.