Feature Selection
Showing 15 of 15 projects
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Python library providing extensions and utilities for data science and machine learning tasks.
A machine learning framework for developing high-frequency trading strategies using full orderbook tick data.
A Python library for feature engineering and selection with scikit-learn compatible transformers.
A unified interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
Python implementation of the Boruta all-relevant feature selection method with scikit-learn compatibility.
An open-source Python repository providing around 40 feature selection algorithms for machine learning applications.
Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
Automatically builds high-performance interpretable machine learning models with minimal features using a single line of code.
A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for machine learning.
A Python library for feature selection using nature-inspired wrapper algorithms like particle swarm, grey wolf, and genetic optimization.
A fast feature selection algorithm for tree-based models like XGBoost, designed to outperform Boruta in speed and performance.
A Python package for automated univariate and bivariate data analysis and visualization to streamline machine learning workflows.
A method for selecting interpretable feature subsets from complex models using mutual information optimization.
A Julia wrapper for fitting Lasso and ElasticNet GLM models using the glmnet Fortran library.
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