An example is a single observation used by a machine learning system, usually represented as one row or instance with one or more features. In supervised learning, an example may include a label for training; in inference, it may contain only input features.
The values of one row of features and possibly a label. Examples in supervised learning fall into two general categories: - A labeled example consists of one or more features and a label. Labeled examples are used during training. - An unlabeled example consists of one or more features but no label. Unlabeled examples are used during inference. For instance, suppose you are training a model to determine the influence of weather conditions on student test scores. Here are three labeled examples: Label | Humidity Test score | ------| 47 Good | 34 Excellent | 92 Poor | Here are three unlabeled examples: Humidity | ------| 62 | 47 | 41 | The row of a dataset is typically the raw source for an example. That is, an example typically consists of a subset of the columns in the dataset. Furthermore, the features in an example can also include synthetic features, such as feature crosses. See Supervised Learning in the Introduction to Machine Learning course for more information.