dot-fit.mrmr: Minimum redundancy, maximum relevance feature selection for...

In tidyfit: Regularized Linear Modeling with Tidy Data

Minimum redundancy, maximum relevance feature selection for tidyfit

Description

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

Usage

## S3 method for class 'mrmr'
.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)

• feature_count (number of features to select)

• solution_count (ensemble size)

The MRMR algorithm is estimated using the mRMRe::mRMR.ensemble function. See ?mRMR.ensemble for more details.

Implementation

Use with regress for regression problems and with classify for classification problems. The selected features can be obtained using coef.

The MRMR objects have no predict and related methods.

Value

A fitted 'tidyFit' class model.

Author(s)

Johann Pfitzinger

References

De Jay N, Papillon-Cavanagh S, Olsen C, Bontempi G and Haibe-Kains B (2012). mRMRe: an R package for parallelized mRMR ensemble feature selection.

See Also

m methods

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns
data <- dplyr::filter(data, Industry == "HiTec")
data <- dplyr::select(data, SMB, HML, RMW, CMA, Return)

## Not run: 
fit <- m("mrmr", Return ~ ., data, feature_count = 2)

# Retrieve selected features
coef(fit)

## End(Not run)

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