details_logistic_reg_glm: Logistic regression via glm

In parsnip: A Common API to Modeling and Analysis Functions

Logistic regression via glm

Description

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():

linear_reg() %>% 
  set_engine("glm", family = stats::binomial(link = "probit")) %>% 
  translate()

## Linear Regression Model Specification (regression)
## 
## 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.

References

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

parsnip documentation built on June 24, 2024, 5:14 p.m.