Supervised learning algorithms are a class of algorithms that utilize data samples (also called training samples) and corresponding outputs (or labels) to infer a mapping function between the two. The inferred mapping function or the learned function is the output of this training process. The learned function is then utilized to correctly map new and unseen data points (input elements) to test the performance of the learned function.

Some key concepts for supervised learning algorithms are as follows:

• Training dataset: The training samples and corresponding outputs utilized during the training process are termed as training data. Formally, a training dataset is a two-element tuple consisting of an input element (usually a vector) and a corresponding output element or signal.
• Test dataset: The unseen dataset that is utilized to test the performance of the learned function. This dataset is also a two-element tuple containing input data points and corresponding output signals. Data points in this set are not used for the training phase (this dataset is further divided into the validation set as well; we will discuss this in more detail in subsequent chapters).
• Learned function: This is the output of the training phase. Also termed as inferred function or the model. This function is inferred based on the training examples (input data points and their corresponding outputs) from the training dataset. An ideal model/learned function would learn the mapping in such a way that the results can be generalized for unseen data as well.

There are various supervised learning algorithms available. Based on the use case requirements, they can be majorly categorize into classification and regression models.