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Mean Squared Error (MSE): |

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## 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