PART 2: Supervised Machine Learning
Machine learning (ML) develops algorithms to identify patterns in data (unsupervised ML) or make predictions and inferences (supervised ML).
Supervised ML trains the machine to learn from prior examples to predict either a categorical outcome (classification) or a numeric outcome (regression), or to infer the relationships between the outcome and its explanatory variables.
Two early forms of supervised ML are linear regression (OLS) and generalized linear models (GLM) (Poisson and logistic regression). These methods have been improved with advanced linear methods, including stepwise selection, regularization (ridge, lasso, elastic net), principal components regression, and partial least squares. With greater computing capacity, non-linear models are now in use, including polynomial regression, step functions, splines, and generalized additive models (GAM). Decision trees (bagging, random forests, and, boosting) are additional options for regression and classification, and support vector machines is an additional option for classification.