What is the term used to describe the squared difference between a target and an estimate?

The term that refers specifically to the squared difference between a target value and an estimate is known as Squared Error. This concept is fundamental in statistical analysis and is frequently used in regression analysis and various predictive modeling evaluations.

Squared Error quantifies how far the predicted values deviate from the actual target values by taking the difference, squaring it, and thereby eliminating any negative values. This method emphasizes larger errors more than smaller ones due to the squaring effect, which is beneficial when assessing model performance since it allows large deviations to have a disproportionately larger impact on the overall error metric.

In contrast, misclassification refers to instances when a predicted class does not match the actual class in classification problems, making it unsuitable for describing squared differences. Variance probability is not directly relevant to the context of estimating and comparing numerical values. Prediction error, while related, generally encompasses the difference itself but does not specifically denote the squared difference that Squared Error does. Thus, the correct term to describe the squared difference is Squared Error, as it accurately reflects the mathematical operation and its implications in modeling contexts.