glmnet::glmnet()
uses penalized maximum likelihood to fit a model for
count data.
Details
For this engine, there is a single mode: regression
Tuning Parameters
This model has 2 tuning parameters:
penalty
: Amount of Regularization (type: double, default: see below)mixture
: Proportion of Lasso Penalty (type: double, default: 1.0)
The penalty
parameter has no default and requires a single numeric
value. For more details about this, and the glmnet
model in general,
see glmnet-details. As for mixture
:
mixture = 1
specifies a pure lasso model,mixture = 0
specifies a ridge regression model, and0 < mixture < 1
specifies an elastic net model, interpolating lasso and ridge.
Translation from parsnip to the original package
The poissonreg extension package is required to fit this model.
library(poissonreg)
poisson_reg(penalty = double(1), mixture = double(1)) %>%
set_engine("glmnet") %>%
translate()
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.
Predictors should have the same scale. One way to achieve this is to
center and scale each so that each predictor has mean zero and a
variance of one. By default, glmnet::glmnet()
uses the argument
standardize = TRUE
to center and scale the data.
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.
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.