feature-selection

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

machine-learning deep-learning gpu feature-selection nvidia recommendation-system recommender-system preprocessing feature-engineering

UpdatedOct 23, 2025
Python

aerdem4 /lofo-importance

Star855

Leave One Feature Out Importance

data-science machine-learning feature-selection feature-importance explainable-ai

UpdatedFeb 14, 2025
Python

alteryx /evalml

Star834

EvalML is an AutoML library written in python.

data-science machine-learning optimization feature-selection model-selection feature-engineering hyperparameter-tuning automl

UpdatedDec 6, 2025
Python

ashishpatel26 /Amazing-Feature-Engineering

Star757

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

data-science machine-learning data-mining deep-learning scikit-learn data-visualization feature-selection feature-extraction data-analysis data-scientists feature-engineering features feature-scaling

UpdatedJun 29, 2025
Jupyter Notebook

duxuhao /Feature-Selection

Star681

Features selector based on the self selected-algorithm, loss function and validation method

data-science machine-learning feature-selection feature-extraction feature-engineering greedy-search feature-importance

UpdatedMay 8, 2019
Python

AutoViML /featurewiz

Star674

Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

feature-selection feature-extraction xgboost feature-engineering autoencoders categorical-variables mrmr rfe featuretools rfecv feature-encoding best-encoders feature-engg

UpdatedFeb 19, 2025
Python

smazzanti /mrmr

Star617

mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.

data-science machine-learning feature-selection mlops

UpdatedNov 19, 2024
Python

cerlymarco /shap-hypetune

Star583

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

feature-selection model-selection xgboost hyperparameter-optimization lightgbm parameter-tuning shap

UpdatedJun 8, 2024
Jupyter Notebook

cod3licious /autofeat

Star535

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

machine-learning linear-regression feature-selection feature-engineering automl automated-machine-learning machine-learning-models automated-data-science automated-feature-engineering

UpdatedDec 17, 2025
Python

akanz1 /klib

Sponsor

Star520

Easy to use Python library of customized functions for cleaning and analyzing data.

python data-science data-visualization feature-selection data-analysis klib data-preprocessing data-cleaning

UpdatedDec 1, 2025
Python

abess-team /abess

Star489

Fast Best-Subset Selection Library

python machine-learning r linear-regression scikit-learn high-dimensional-data feature-selection logistic-regression cox-regression principal-component-analysis classification-algorithm ordinal-regression poisson-regression sure-independence-screening multitask-learning sparse-principal-component-analysis robust-principal-component-analysis polynomial-algorithm best-subset-selection

UpdatedDec 6, 2025
C++

Desbordante /desbordante-core

Star449

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.