Table of Contents
Mean Squared Error (MSE) is a common metric used to evaluate the performance of regression models. It measures the average squared difference between actual and predicted values. Calculating MSE involves a few straightforward steps that help in understanding how well a model predicts data points.
Step 1: Gather Actual and Predicted Values
Collect the actual values from the dataset and the predicted values generated by the regression model. Both should be in the same order to ensure accurate calculation.
Step 2: Calculate the Differences
For each data point, subtract the predicted value from the actual value. This gives the error for each point.
Step 3: Square the Errors
Square each error to eliminate negative values and emphasize larger errors. This is done by multiplying each error by itself.
Step 4: Calculate the Mean
Add all the squared errors together and divide by the total number of data points. This gives the mean of the squared errors, which is the MSE.
Summary of Calculation
- Gather actual and predicted values
- Calculate the error for each data point
- Square each error
- Sum all squared errors
- Divide by the number of data points