Supervised learning is the simplest and most well-known automatic learning task. It is based on a number of pre-defined examples, in which the category to which each of the inputs should belong is already known. Figure 2 shows a typical workflow of supervised learning.
An actor (for example, an ML practitioner, data scientist, data engineer, ML engineer, and so on) performs Extraction Transformation Load (ETL) and the necessary feature engineering (including feature extraction, selection, and so on) to get the appropriate data having features and labels. Then he does the following:
- Splits the data into training, development, and test sets
- Uses the training set to train an ML model
- The validation set is used to validate ...