Machine Learning and Data Science Blueprints for Finance

Chapter 4. Supervised Learning: Models and Concepts

Supervised learning is an area of machine learning where the chosen algorithm tries to fit a target using the given input. A set of training data that contains labels is supplied to the algorithm. Based on a massive set of data, the algorithm will learn a rule that it uses to predict the labels for new observations. In other words, supervised learning algorithms are provided with historical data and asked to find the relationship that has the best predictive power.

There are two varieties of supervised learning algorithms: regression and classification algorithms. Regression-based supervised learning methods try to predict outputs based on input variables. Classification-based supervised learning methods identify which category a set of data items belongs to. Classification algorithms are probability-based, meaning the outcome is the category for which the algorithm finds the highest probability that the dataset belongs to it. Regression algorithms, in contrast, estimate the outcome of problems that have an infinite number of solutions (continuous set of possible outcomes).

In the context of finance, supervised learning models represent one of the most-used class of machine learning models. Many algorithms that are widely applied in algorithmic trading rely on supervised learning models because they can be efficiently trained, they are relatively robust to noisy financial data, and they have strong links to the theory of finance. ...