Mean Squared Error
A metric for evaluating the performance of predictive models by calculating the average of the squares of the differences between observed and predicted values.
In the context of AI and particularly ML, Mean Squared Error (MSE) is employed as a loss function to measure the quality of an estimator or a predictive model, quantifying the deviation between predicted values from a model and the actual observed data points. MSE is significant due to its sensitivity to large errors, effectively penalizing large discrepancies more than smaller ones, and is commonly used in regression analysis to optimize models by minimizing this error metric during the training process. Its mathematical formulation requires squaring the difference between each predicted and actual value, summing those squares, and then averaging them, which not only ensures non-negative values but also emphasizes larger errors, making it a useful tool for many applications where precision is critical.
The concept of Mean Squared Error predates modern computational methods, with its mathematical roots dating back to statistical analyses in the early 20th century, gaining popularity in the realm of AI and ML during the late 20th century as models became more sophisticated and required effective measures for error quantification and optimization.
While no single individual is credited with the invention of MSE, Karl Pearson's work in statistics laid fundamental groundwork, while its integration into AI and ML frameworks has been shaped by numerous contributors across the fields of statistics, computer science, and applied mathematics who have helped standardize its use in model evaluation techniques.