glmnet::glmnet() 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 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 = 1specifies a pure lasso model,mixture = 0specifies a ridge regression model, and0 < mixture < 1specifies an elastic net model, interpolating lasso and ridge.
Translation from parsnip to the original package
logistic_reg(penalty = double(1), mixture = double(1)) %>%
set_engine("glmnet") %>%
translate()## Logistic Regression Model Specification (classification)
##
## Main Arguments:
## penalty = 0
## mixture = double(1)
##
## Computational engine: glmnet
##
## Model fit template:
## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## alpha = double(1), family = "binomial")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.
Examples
The “Fitting and Predicting with parsnip” article contains
examples
for logistic_reg() with the "glmnet" engine.