details_linear_reg_glmnet: Linear regression via glmnet

In parsnip: A Common API to Modeling and Analysis Functions

Linear regression via glmnet

Description

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(), 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 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.

parsnip documentation built on June 24, 2024, 5:14 p.m.