dot-fit.rf: Random Forest regression or classification for 'tidyfit'

In tidyfit: Regularized Linear Modeling with Tidy Data

Random Forest regression or classification for tidyfit

Description

Fits a random forest on a 'tidyFit' R6 class. The function can be used with regress and classify.

Usage

## S3 method for class 'rf'
.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:

• ntree (number of trees)

• mtry (number of variables randomly sampled at each split)

Important method arguments (passed to m)

The function provides a wrapper for randomForest::randomForest. See ?randomForest for more details.

Implementation

The random forest is always fit with importance = TRUE. The feature importance values are extracted using coef().

Value

A fitted 'tidyFit' class model.

Author(s)

Johann Pfitzinger

References

Liaw, A. and Wiener, M. (2002). Classification and Regression by randomForest. R News 2(3), 18–22.

See Also

.fit.svm, .fit.boost and m methods

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns
data <- dplyr::filter(data, Industry == "HiTec")
data <- dplyr::select(data, -Date, -Industry)

# Stand-alone function
fit <- m("rf", Return ~ ., data)
fit

# Within 'regress' function
fit <- regress(data, Return ~ ., m("rf"))
explain(fit)

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