`glmnet::glmnet()`` uses penalized maximum likelihood to fit a model for count data.

For this engine, there is a single mode: regression

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)

A value of `mixture = 1`

corresponds to a pure lasso model, while
`mixture = 0`

indicates ridge regression.

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.

```
poisson_reg(penalty = double(1), mixture = double(1)) %>%
set_engine("glmnet") %>%
translate()
```

```
## Poisson Regression Model Specification (regression)
##
## 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 = "poisson")
```

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.model_spec()`

, 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.