# Supervised Machine Learning

*My handbook*

*2021-08-25*

# Intro

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.