A fast xgboost feature selection algorithm
☆233Apr 1, 2021Updated 4 years ago
Alternatives and similar repositories for BoostARoota
Users that are interested in BoostARoota are comparing it to the libraries listed below
Sorting:
- Python implementations of the Boruta all-relevant feature selection method.☆1,620Nov 13, 2025Updated 3 months ago
- A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.☆420Feb 10, 2023Updated 3 years ago
- XGBoost Feature Interactions Reshaped☆433Nov 10, 2017Updated 8 years ago
- A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.☆646Feb 19, 2024Updated 2 years ago
- Data Version Control (DVC) tutorial 2. Iris Demo Project☆13Sep 30, 2022Updated 3 years ago
- Genetic feature selection module for scikit-learn☆325Jan 20, 2024Updated 2 years ago
- mimic calibration☆21Oct 16, 2019Updated 6 years ago
- Feature Selection using Genetic Algorithm (DEAP Framework)☆376Feb 21, 2023Updated 3 years ago
- scikit-learn compatible implementation of stability selection.☆214Jun 5, 2023Updated 2 years ago
- Two custom ways of calculating cross_val_score☆37Jul 26, 2017Updated 8 years ago
- open-source feature selection repository in python☆1,565Jul 11, 2024Updated last year
- Extension of the awesome XGBoost to linear models at the leaves☆83Jun 24, 2019Updated 6 years ago
- Hyper-parameter optimization for sklearn☆1,646Apr 15, 2025Updated 10 months ago
- Fast, accurate, lightweight, multi-core ML in Python, leveraging Vowpal Wabbit☆21May 26, 2018Updated 7 years ago
- Feature selection library in python☆146Aug 3, 2025Updated 7 months ago
- autosklearn-zeroconf is a fully automated binary classifier. It is based on the AutoML challenge winner auto-sklearn. Give it a dataset w…☆171Oct 30, 2019Updated 6 years ago
- Fit Lasso model to binary rules created from tree ensembles☆12Aug 2, 2017Updated 8 years ago
- A library for debugging/inspecting machine learning classifiers and explaining their predictions☆2,772Feb 10, 2026Updated 3 weeks ago
- Feature Selection in R using glmnet-lasso, xgboost and ranger☆56Aug 9, 2024Updated last year
- A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.☆126Jun 10, 2025Updated 8 months ago
- 🧲 Multi-step adaptive estimation for reducing false positive selection in sparse regressions☆13Jul 21, 2024Updated last year
- алгоритм, занявший второе место на конкурсе http://cardioqvark.ru/challenge/☆11Apr 3, 2016Updated 9 years ago
- Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one…☆383Jan 8, 2022Updated 4 years ago
- ML-Ensemble – high performance ensemble learning☆863Nov 13, 2023Updated 2 years ago
- A stacked generalization framework. Built on top of scikit learn☆58Dec 27, 2022Updated 3 years ago
- A Python wrapper for LibFFM☆122Jun 16, 2019Updated 6 years ago
- In this repository we test AutoML approaches for time-series forecasting☆13Aug 2, 2018Updated 7 years ago
- greedy feature selection based on ROC AUC☆125Apr 7, 2014Updated 11 years ago
- Python package for stacking (machine learning technique)☆701Nov 1, 2025Updated 4 months ago
- ☆15Jul 17, 2018Updated 7 years ago
- Statistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks (currently Python & R)☆20Feb 22, 2026Updated last week
- more data science resources☆14Jun 4, 2022Updated 3 years ago
- A library of sklearn compatible categorical variable encoders☆2,484Updated this week
- InfiniteBoost: building infinite ensembles with gradient descent☆183Sep 17, 2018Updated 7 years ago
- Search the best feature subset for you classification mode☆114May 26, 2020Updated 5 years ago
- A new version of phraug, which is a set of simple Python scripts for pre-processing large files☆207Jul 12, 2018Updated 7 years ago
- An extension of CatBoost to probabilistic modelling☆149Oct 27, 2023Updated 2 years ago
- (deprecated) A fast and memory-efficient Python data engineering framework for machine learning.☆33Sep 30, 2019Updated 6 years ago
- XGBoost for label-imbalanced data: XGBoost with weighted and focal loss functions☆340Feb 14, 2024Updated 2 years ago