Supervised Learning

The supervised learning paradigm [34] is perhaps the most popular method in the machine learning community. In supervised learning, one has access to the ground truth for samples contained in the training, validation, and test data sets. Ground truth represents “true” or “correct” labels for the input dataset. Expert help may be needed to obtain the correct labels for the data (medical image labeling, for example). The ML model is “trained” using a labeled input dataset termed training data. Once the model achieves the desired performance on training data, the trained model is then used to perform inference on unseen data. The data that has not been used for training and thus unseen by the model is termed test data.