Machine Learning

Exam Focus

This topic review discusses the terminology used in advanced statistical models collectively referred to as machine learning. Be familiar with this terminology and the different types of models, their applications in investment decision making, and their limitations. Specifically, be able to identify the appropriate algorithm that is most suitable for a given problem.

MACHINE LEARNING

The statistical models we have discussed so far rely on a set of assumptions about the distribution of the underlying data. Machine learning (ML) requires no such assumptions. Very broadly, ML is defined as the use of algorithms to make decisions by generalizing (or finding patterns) in a given data set.

ML performs better than standard statistical approaches when dealing with a large number of variables (high dimension) and when the relationships are nonlinear.

ML terms are as follows:

• Target variable. This is the dependent variable (i.e., the y variable). Target variables can be continuous, categorical, or ordinal.
• Features. These are the independent variables (i.e., the x variables).
• Training dataset. This is the sample used to fit the model. Hyperparameter.
• This is a model input specified by the researcher.