The formula to calculate the squared error (SE) is:
\[ \text{SE} = (\text{AV} - \text{PV})^2 \]
Where:
Squared error is a measure of the discrepancy between the actual value and the predicted value in statistical models and machine learning algorithms. It is used as a loss function to optimize models during training. The squared error is always non-negative, and a value of zero indicates a perfect prediction. It is sensitive to outliers due to the squaring of the error term.
Let's assume the following values:
Using the formula:
\[ \text{SE} = (10 - 8)^2 = 2^2 = 4 \]
The Squared Error (SE) is 4.
