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| Mean Squared Error (MSE): |
Free Online MSE Calculator – Calculate your Mean Squared Error (MSE) with our user-friendly MSE calculator! Easily assess the accuracy of your predictions by comparing them to actual data.
Mean Square Error(MSE) Formula
For a given dataset with ‘n’ data points, the formula for Mean Squared Error (MSE) is as follows:
MSE = Σ(yi – pi)² / n
- MSE = Mean Square Error
- Yi = Observed value / Actual value
- Pi = Predicted value
- n = Total number of data points
How to calculate MSE?
To calculate MSE, you take the squared difference between the actual and predicted values for each data point, sum up these squared differences, and then divide by the total number of data points ‘n’.
Example – Suppose we have a dataset of actual (observed) values and corresponding predicted values as follows:
Actual Values (Yi): [10, 12, 15, 8, 20]
Predicted Values (Pi): [11, 13, 14, 9, 18]
Step 1: Gather the data
Step 2: Calculate the squared differences
Squared Differences:
(10 – 11)² = 1
(12 – 13)² = 1
(15 – 14)² = 1
(8 – 9)² = 1
(20 – 18)² = 4
Step 3: Sum the squared differences
Sum of Squared Differences = 1 + 1 + 1 + 1 + 4 = 8
Step 4: Divide by the number of data points
MSE = (1/5) * 8 = 1.6
Mean Squared Error (MSE) for this dataset is 1.6