glmnet::glmnet() fits a model that uses linear predictors to predict
multiclass data using the multinomial distribution.
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
multinom_reg(penalty = double(1), mixture = double(1)) %>%
set_engine("glmnet") %>%
translate()## Multinomial 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 = "multinomial")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.
Examples
The “Fitting and Predicting with parsnip” article contains
examples
for multinom_reg() with the "glmnet" engine.
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.
Sparse Data
This model can utilize sparse data during model fitting and prediction.
Both sparse matrices such as dgCMatrix from the Matrix package and
sparse tibbles from the sparsevctrs package are supported. See
sparse_data for more information.
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.