details_gen_additive_mod_mgcv | R Documentation |
mgcv::gam()
fits a generalized linear model with additive smoother terms
for continuous predictors.
For this engine, there are multiple modes: regression and classification
This model has 2 tuning parameters:
select_features
: Select Features? (type: logical, default: FALSE)
adjust_deg_free
: Smoothness Adjustment (type: double, default: 1.0)
gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>% set_engine("mgcv") %>% set_mode("regression") %>% translate()
## GAM Model Specification (regression) ## ## Main Arguments: ## select_features = logical(1) ## adjust_deg_free = numeric(1) ## ## Computational engine: mgcv ## ## Model fit template: ## mgcv::gam(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), ## select = logical(1), gamma = numeric(1))
gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>% set_engine("mgcv") %>% set_mode("classification") %>% translate()
## GAM Model Specification (classification) ## ## Main Arguments: ## select_features = logical(1) ## adjust_deg_free = numeric(1) ## ## Computational engine: mgcv ## ## Model fit template: ## mgcv::gam(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), ## select = logical(1), gamma = numeric(1), family = stats::binomial(link = "logit"))
This model should be used with a model formula so that smooth terms can be specified. For example:
library(mgcv) gen_additive_mod() %>% set_engine("mgcv") %>% set_mode("regression") %>% fit(mpg ~ wt + gear + cyl + s(disp, k = 10), data = mtcars)
## parsnip model object ## ## ## Family: gaussian ## Link function: identity ## ## Formula: ## mpg ~ wt + gear + cyl + s(disp, k = 10) ## ## Estimated degrees of freedom: ## 7.52 total = 11.52 ## ## GCV score: 4.225228
The smoothness of the terms will need to be manually specified (e.g.,
using s(x, df = 10)
) in the formula. Tuning can be accomplished using
the adjust_deg_free
parameter.
When using a workflow, pass the model formula to
workflows::add_model()
’s formula
argument,
and a simplified preprocessing formula elsewhere.
spec <- gen_additive_mod() %>% set_engine("mgcv") %>% set_mode("regression") workflow() %>% add_model(spec, formula = mpg ~ wt + gear + cyl + s(disp, k = 10)) %>% add_formula(mpg ~ wt + gear + cyl + disp) %>% fit(data = mtcars) %>% extract_fit_engine()
## ## Family: gaussian ## Link function: identity ## ## Formula: ## mpg ~ wt + gear + cyl + s(disp, k = 10) ## ## Estimated degrees of freedom: ## 7.52 total = 11.52 ## ## GCV score: 4.225228
To learn more about the differences between these formulas, see
?model_formula
.
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.
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.
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.
Ross, W. 2021. Generalized Additive Models in R: A Free, Interactive Course using mgcv
Wood, S. 2017. Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC.
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