Enter the actual and predicted values into the calculator to determine the squared error. This calculator helps in assessing the accuracy of predictions in statistics and machine learning.
Squared Error Formula
The following formula is used to calculate the squared error (SE):
SE = (AV - PV)^2
Variables:
- SE is the squared error
- AV is the actual value
- PV is the predicted value
To calculate the squared error, subtract the predicted value from the actual value, and then square the result.
What is Squared Error?
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.
How to Calculate Squared Error?
The following steps outline how to calculate the Squared Error.
- First, determine the actual value (AV).
- Next, determine the predicted value (PV).
- Use the formula SE = (AV - PV)^2 to calculate the squared error.
- Finally, compare the squared error with other predictions to assess the model's accuracy.
- After inserting the variables and calculating the result, check your answer with the calculator above.
Example Problem :
Use the following variables as an example problem to test your knowledge.
Actual value (AV) = 100
Predicted value (PV) = 90
