What is Mean Squared Error?

Can you explain what Mean Squared Error is?

Mean Squared Error (MSE) is a measure of the average squared differences between predicted and actual values.

Why is MSE important in machine learning?

MSE is crucial because it quantifies how well a model's predictions match the actual data, helping to evaluate model performance.

How do you calculate MSE?

To calculate MSE, take the average of the squared differences between predicted and actual values: MSE = (1/n) * Σ(actual - predicted)².

Can you give an example of MSE in a regression problem?

Sure! If you have actual values [3, -0.5, 2] and predicted values [2.5, 0.0, 2], the MSE would be calculated as follows: MSE = (1/3) * ((3-2.5)² + (-0.5-0)² + (2-2)²) = 0.25.