dot-fit.lasso: Lasso regression and classification for 'tidyfit'

In tidyfit: Regularized Linear Modeling with Tidy Data

Lasso regression and classification for tidyfit

Description

Fits a linear regression or classification with L1 penalty on a 'tidyFit' R6 class. The function can be used with regress and classify.

Usage

## S3 method for class 'lasso'
.fit(self, data = NULL)

Arguments

self	a 'tidyFit' R6 class.
data	a data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).

Details

Hyperparameters:

• lambda (L1 penalty)

Important method arguments (passed to m)

The Lasso regression is estimated using glmnet::glmnet with alpha = 1. See ?glmnet for more details. For classification pass family = "binomial" to ... in m or use classify.

Implementation

If the response variable contains more than 2 classes, a multinomial response is used automatically.

Features are standardized by default with coefficients transformed to the original scale.

If no hyperparameter grid is passed (is.null(control$lambda)), dials::grid_regular() is used to determine a sensible default grid. The grid size is 100. Note that the grid selection tools provided by glmnet::glmnet cannot be used (e.g. dfmax). This is to guarantee identical grids across groups in the tibble.

Value

A fitted 'tidyFit' class model.

Author(s)

Johann Pfitzinger

References

Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. URL https://www.jstatsoft.org/v33/i01/.

See Also

.fit.enet, .fit.ridge, .fit.adalasso and m methods

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns

# Stand-alone function
fit <- m("lasso", Return ~ ., data, lambda = 0.5)
fit

# Within 'regress' function
fit <- regress(data, Return ~ ., m("lasso", lambda = c(0.1, 0.5)),
               .mask = c("Date", "Industry"))
coef(fit)

tidyfit documentation built on Oct. 3, 2024, 5:06 p.m.