dot-fit.relief: ReliefF and RReliefF feature selection algorithm for...

In tidyfit: Regularized Linear Modeling with Tidy Data

ReliefF and RReliefF feature selection algorithm for tidyfit

Description

Selects features for continuous or factor data using ReliefF on a 'tidyFit' R6 class. The function can be used with regress and classify.

Usage

## S3 method for class 'relief'
.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:

None. Cross validation not applicable.

Important method arguments (passed to m)

• estimator (selection algorithm to use (default is 'ReliefFequalK'))

The ReliefF algorithm is estimated using the CORElearn::attrEval function. See ?attrEval for more details.

Implementation

Use with regress for regression problems and with classify for classification problems. coef returns the score for each feature. Select the required number of features with the largest scores.

The Relief objects have no predict and related methods.

Value

A fitted 'tidyFit' class model.

Author(s)

Johann Pfitzinger

References

Robnik-Sikonja M, Savicky P (2021). CORElearn: Classification, Regression and Feature Evaluation. R package version 1.56.0, https://CRAN.R-project.org/package=CORElearn.

See Also

.fit.mrmr 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("relief", Return ~ ., data)
coef(fit)

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

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