Our Mean Squared Error (MSE) Calculator helps you quickly assess the accuracy of your regression models. Easily measure the average squared difference between predicted and actual values to understand model performance. Ideal for data scientists, analysts, and students looking for precise error measurement tools.
Formula:
MSE = (1/n) × ∑ (Yi - Ŷi)2
Where:
n: Number of data points
Yi: Actual observed value for the i-th point
Ŷi: Predicted value for the i-th point