Also known as: ground truth (GT) Β· GT
Reality. The thing that actually happened. For example, consider a binary classification model that predicts whether a student in their first year of university will graduate within six years. Ground truth for this model is whether or not that student actually graduated within six years. We assess model quality against ground truth. However, ground truth is not always completely, well, truthful. For example, consider the following examples of potential imperfections in ground truth: - In the graduation example, are we certain that the graduation records for each student are always correct? Is the university's record-keeping flawless? - Suppose the label is a floating-point value measured by instruments (for example, barometers). How can we be sure that each instrument is calibrated identically or that each reading was taken under the same circumstances? - If the label is a matter of human opinion, how can we be sure that each human rater is evaluating events in the same way? To improve consistency, expert human raters sometimes intervene.
βValue of the target variable for a particular item of labelled input data. The term ground truth does not imply that the labelled input data consistently corresponds to the real-world value of the target variables.β (ISO/IEC 22989).
The observations used in the machine learning (ML) model training process that include the correct value for the target attribute. To train an ML model to predict house sales prices, the input observations would typically include prices of previous house sales in the area. The sale prices of these houses constitute the ground truth.