In the realm of machine learning (ML), ground truth represents the accurate or factual labels and annotations corresponding to a specific dataset. It serves as a fundamental benchmark for assessing the performance of an ML model, as well as for training and validating the model.
For instance, when developing an ML model for classifying animal images, the ground truth would consist of the correct labels for each image, like "cat", "dog", or "bird". The model is trained on a dataset containing both the images and their respective ground truth labels. Subsequently, its performance is gauged by how precisely it can predict the correct labels for novel, unseen images.
Especially for large datasets, acquiring ground truth labels can be an arduous and time-consuming process. It often involves painstakingly reviewing and annotating each instance within the dataset, which demands a significant amount of time and effort. In certain situations, automated methods might be employed to generate ground truth labels; however, these approaches may be less reliable and typically require additional manual review and rectification.