`stats::lm()`

uses ordinary least squares to fit models with numeric outcomes.

For this engine, there is a single mode: regression

This engine has no tuning parameters.

linear_reg() %>% set_engine("lm") %>% translate()

## Linear Regression Model Specification (regression) ## ## Computational engine: lm ## ## Model fit template: ## stats::lm(formula = missing_arg(), data = missing_arg(), weights = missing_arg())

Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via
`fit.model_spec()`

, parsnip will
convert factor columns to indicators.

The “Fitting and Predicting with parsnip” article contains
examples
for `linear_reg()`

with the `"lm"`

engine.

Kuhn, M, and K Johnson. 2013.

*Applied Predictive Modeling*. Springer.