Feature-engine
Feature-engine: A Python package for feature engineering for machine learning - Published in JOSS (2021)
fseval
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms - Published in JOSS (2022)
UBayFS
UBayFS: An R Package for User Guided Feature Selection - Published in JOSS (2023)
RENT
RENT: A Python Package for Repeated Elastic Net Feature Selection - Published in JOSS (2021)
CAST
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
deepfastmlu
Machine learning utilities to help speed up the prototyping process.
zoish
Zoish is a Python package that streamlines machine learning by leveraging SHAP values for feature selection and interpretability, making model development more efficient and user-friendly
pyimpetus
PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features
autofeat
Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
upgini
Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
https://github.com/microsoft/finnts
Microsoft Finance Time Series Forecasting Framework (FinnTS) is a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data.
https://github.com/cumbof/chopin2
Domain-Agnostic Supervised Learning with Hyperdimensional Computing
skrebate
A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
FSelectorRcpp
Rcpp (free of Java/Weka) implementation of FSelector entropy-based feature selection algorithms with a sparse matrix support
pymrmr
Python3 binding to mRMR Feature Selection algorithm (currently not maintained)
https://github.com/critical-infrastructure-systems-lab/matlab_iterative_input_selection
MatLab implementation of the Iterative Input Selection (IIS) algorithm proposed by Galelli and Castelletti (2013).
https://github.com/habedi/feature-factory
A feature engineering library for Rust 🦀 with Python bindings 🐍
https://github.com/critical-infrastructure-systems-lab/iterative_input_selection
MatLab / C implementation of the Iterative Input Selection (IIS) algorithm proposed by Galelli and Castelletti (2013). The underlying model (i.e. Extremely Randomized Trees) relies on C code that makes it more computationally efficient.
fcbf
Categorical feature selection based on information theoretical considerations
SPSP
A novel approach for feature selection based on the entire solution paths rather than the choice of a single tuning parameter, which significantly improves the accuracy of the selection.
https://github.com/ajayarunachalam/msda
Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector
https://github.com/biodataanalysisgroup/kmeranalyzer
An alignment-free method capable of processing and counting k-mers in a reasonable time, while evaluating multiple values of the k parameter concurrently.
mafese
Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python
fbp
Frequency Based Pruning (FBP) is a feature selection algorithm based upon maximizing the Youden J statistic. FBP intelligently enumerates through combinations of features, using the frequency of smaller patterns to prune away large regions of the solution space.
https://github.com/yuenshingyan/ForwardStepwiseFeatureSelection
ForwardStepwiseFeatureSelection
behavioralproject
Classify TD vs ASD according to SRS behavioral report severity score. ABIDE II data set is utilized for training and testing. Freesurfer v6 is utilized for sMRI volumes preprocessing and features extraction.
gsta
GSTA: Gated Spatial-Temporal Attention Approach for Travel Time Prediction
fedfs
Federated Feature Selection as in the paper https://arxiv.org/abs/2109.11323
l0
Scalable reinforcement learning pipeline for general-purpose agents. Build and train agents using the Notebook Agent (NB-Agent) in a flexible environment. 🐙🚀
https://github.com/dangeospatial/gee_wetland_classification
A sample workflow for classifying wetlands in Google Earth Engine. Uses data from multiple sources.
additive-sparse-boost-regression
A Python Package for a Sparse Additive Boosting Regressor
stabilityselection
Perform stability selection in matlab using a variety of feature selection methods
compressedsensing.jl
Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian learning, and basis pursuit.
https://github.com/critical-infrastructure-systems-lab/multi-objective-feature-selection
MatLab implementation of W-QEISS, F-QEISS and W-MOSS: three algorithms for the selection of (quasi) equally informative subsets
PyCCEA
PyCCEA: A Python package of cooperative co-evolutionary algorithms for feature selection in high-dimensional data - Published in JOSS (2025)
https://github.com/desbordante/desbordante-core
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.