Mean Squared Error

Definition of Mean Squared Error

Mean Squared Error is a statistic used to measure the accuracy of predictions made by a machine learning model. It is calculated by taking the sum of the squared differences between the predicted values and the actual values for each data point, and dividing by the number of data points.

How is Mean Squared Error used?

Mean Squared Error (MSE) is a commonly used measure of the difference between two or more observed values and the predicted values. It is calculated by taking the square of all differences between the observed and predicted values, summing up all squared differences and then finally dividing them by the number of observations. In other words, it measures how closely a set of predicted values match with their corresponding observed values. MSE can be used to evaluate the performance of various predictive models, as it describes the average amount that the model’s predictions deviate from their true values. A lower MSE indicates that its predictions are more accurate compared to one with a higher MSE. Additionally, MSE can be used to compare different types of models with each other in order to find out which model performs better than another. This way, it helps data scientists to determine which type of model fits their data set best and meets their needs for accuracy in prediction. Furthermore, MSE is also an important metric when it comes to machine learning algorithms because it helps in testing out different approaches and configurations on a certain dataset in order to identify which one provides better results.