glmnet::glmnet() uses regularized least squares to fit models with numeric outcomes.

## 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)

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.

### Translation from parsnip to the original package

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


## Linear 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 = "gaussian")


### 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.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.

### Examples

The “Fitting and Predicting with parsnip” article contains examples for linear_reg() with the "glmnet" engine.

### References

• Hastie, T, R Tibshirani, and M Wainwright. 2015. Statistical Learning with Sparsity. CRC Press.

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.