Welcome to dRFEtools’s documentation!

dRFEtools is a package for dynamic recursive feature elimination with scikit-learn.

Authors

• Apuã Paquola

• Kynon Jade Benjamin

• Tarun Katipalli

Package Information

dRFEtools is developed in Python 3.11+.

In addition to scikit-learn, dRFEtools is also built with:

• NumPy

• SciPy

• Pandas

• matplotlib

• plotnine

• statsmodels

Currently, dynamic RFE supports models with coef_ or feature_importances_ attribute.

Features

This package provides several functions to run dynamic recursive feature elimination (dRFE) for random forest and linear model classifier and regression models.

Random Forest Models:

Assume Out-of-Bag (OOB) scoring is enabled.

Linear Models:

Build an internal developmental split.

Measurements for Feature Selection

Classification:

1. Normalized mutual information

2. Accuracy

3. Area under the curve (AUC) ROC curve

Regression:

1. R2 (can be negative if model is arbitrarily worse)

2. Explained variance

3. Mean squared error

Package Structure

The codebase is organized into focused modules:

• dRFEtools.py – core interfaces for random-forest and developmental-set elimination workflows.

• scoring/ – metric implementations for developmental splits and random-forest OOB scoring.

• lowess/ – helpers for smoothing elimination curves and extracting optimal feature counts.

• metrics/ – feature ranking utilities used during elimination.

• plotting.py – visualization helpers re-exported from the top-level package.

• cli.py – command-line entry points for running full dRFE pipelines.

• utils.py – shared helpers for normalizing results and persisting plots.

API Overview

rf_rfe and dev_rfe return standardized dictionaries for each iteration that include the number of retained features, a task-specific metrics mapping, and the indices of surviving features. The same format is consumed by the plotting, LOWESS, and scoring helpers documented in the reference manual below, and is reflected throughout the tutorials for the 0.4.x release.

Table of Contents

• Citation
• Install
• Optimization Examples
  • Optimize classification
  • Optimize regression
  • Optimize classification: multi-class
• Classification Example
  • Define functions to analyze cross-validation
  • Generate classification simulation data
  • Running analysis
  • Session information
• API
  • Core elimination interfaces
  • LOWESS helpers
  • Plotting functions
  • Ranking utilities
  • Developmental set metrics functions
  • Out-of-Bag (OOB) metrics functions
  • Utility helpers
  • Command-line entry points

Indices and tables

• Index

• Module Index

• Search Page