MSE (Mean Squared Error)

Information

• MSE (Mean Squared Error) is a commonly used metric to evaluate the performance of a machine learning model.
• It measures the average squared difference between the predicted values and the actual values.
• To calculate the MSE, you take the sum of the squared differences between the predicted and actual values, and then divide by the number of data points.
• The formula for MSE is: (1/n) * ∑(y_pred - y_actual)^2, where y_pred is the predicted value, y_actual is the actual value, and n is the number of data points.
• MSE is useful for regression problems where the goal is to predict a continuous output variable.
• A lower MSE indicates better performance of the model, as it means that the predicted values are closer to the actual values.
• However, MSE has the disadvantage of being sensitive to outliers in the data, as the squared difference amplifies the effect of outliers.
• To overcome this, other metrics such as MAE (Mean Absolute Error) or RMSE (Root Mean Squared Error) can be used instead.