stats::glm() fits a generalized linear model for binary outcomes. A
linear combination of the predictors is used to model the log odds of an
event.
Details
For this engine, there is a single mode: classification
Tuning Parameters
This engine has no tuning parameters but you can set the family
parameter (and/or link) as an engine argument (see below).
Translation from parsnip to the original package
logistic_reg() %>%
set_engine("glm") %>%
translate()## Logistic Regression Model Specification (classification)
##
## Computational engine: glm
##
## Model fit template:
## stats::glm(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
## family = stats::binomial)To use a non-default family and/or link, pass in as an argument to
set_engine():
logistic_reg() %>%
set_engine("glm", family = stats::binomial(link = "probit")) %>%
translate()## Logistic Regression Model Specification (classification)
##
## Engine-Specific Arguments:
## family = stats::binomial(link = "probit")
##
## Computational engine: glm
##
## Model fit template:
## stats::glm(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
## family = stats::binomial(link = "probit"))Preprocessing requirements
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(), parsnip will
convert factor columns to indicators.
Case weights
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on tidymodels.org.
The fit() and fit_xy() arguments have arguments called
case_weights that expect vectors of case weights.
However, the documentation in stats::glm() assumes
that is specific type of case weights are being used:“Non-NULL weights
can be used to indicate that different observations have different
dispersions (with the values in weights being inversely proportional to
the dispersions); or equivalently, when the elements of weights are
positive integers w_i, that each response y_i is the mean of w_i
unit-weight observations. For a binomial GLM prior weights are used to
give the number of trials when the response is the proportion of
successes: they would rarely be used for a Poisson GLM.”
Saving fitted model objects
This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.
Examples
The “Fitting and Predicting with parsnip” article contains
examples
for logistic_reg() with the "glm" engine.