dot-fit.boost: Gradient boosting regression for 'tidyfit'

In tidyfit: Regularized Linear Modeling with Tidy Data

Gradient boosting regression for tidyfit

Description

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

Usage

## S3 method for class 'boost'
.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:

• mstop (number of boosting iterations)

• nu (step size)

Important method arguments (passed to m)

The gradient boosting regression is performed using mboost::glmboost. See ?glmboost for more details.

Implementation

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

If no hyperparameter grid is passed (is.null(control$mstop) and is.null(control$nu)), the default grid is used with mstop = c(100, 500, 1000, 5000) and nu = c(0.01, 0.05, 0.1, 0.15, 0.2, 0.25).

Value

A fitted 'tidyFit' class model.

A 'tibble'.

Author(s)

Johann Pfitzinger

References

T. Hothorn, P. Buehlmann, T. Kneib, M. Schmid, and B. Hofner (2022). mboost: Model-Based Boosting, R package version 2.9-7,https://CRAN.R-project.org/package=mboost.

See Also

m method

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns

# Stand-alone function
fit <- m("boost", Return ~ ., data, nu = 0.1, mstop = 100)
fit

# Within 'regress' function
fit <- regress(data, Return ~ ., m("boost", nu = c(0.1, 0.05), mstop = 100),
               .mask = c("Date", "Industry"))
coef(fit)

tidyfit documentation built on April 4, 2025, 4:38 a.m.