This tool quantifies the discrepancy between observed and predicted values in a dataset. It operates by calculating the difference between each actual data point and its corresponding predicted value, squaring those differences, and then summing all the squared differences. The resultant single value provides a measure of the overall error in a predictive model. For example, if one were using a model to predict house prices, it would calculate the difference between the model’s price prediction and the actual selling price for each house in the dataset, square each difference, and then add all those squared values together.
The resulting measure is a fundamental metric in regression analysis and statistical modeling. It offers a straightforward way to evaluate the performance of different models or parameters. A lower value indicates a better fit, suggesting the model’s predictions are closer to the actual data. Consequently, minimizing this value is often a primary objective in model selection and optimization. Historically, its application has been pivotal in fields like econometrics, engineering, and data science, enabling researchers and practitioners to fine-tune models and enhance predictive accuracy.
