Alternatives and similar repositories for Boruta-Shap:

Users that are interested in Boruta-Shap are comparing it to the libraries listed below

• cerlymarco / shap-hypetune
  A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
  ☆577Updated 10 months ago
• cod3licious / autofeat
  Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
  ☆513Updated 3 weeks ago
• AutoViML / featurewiz
  Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshad…
  ☆639Updated last month
• StatMixedML / XGBoostLSS
  An extension of XGBoost to probabilistic modelling
  ☆593Updated 9 months ago
• predict-idlab / powershap
  A power-full Shapley feature selection method.
  ☆204Updated 11 months ago
• ThomasBury / arfs
  All Relevant Feature Selection
  ☆132Updated 2 weeks ago
• StatMixedML / LightGBMLSS
  An extension of LightGBM to probabilistic modelling
  ☆302Updated 10 months ago
• AnotherSamWilson / miceforest
  Multiple Imputation with LightGBM in Python
  ☆375Updated 8 months ago
• smazzanti / mrmr
  mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.
  ☆583Updated 4 months ago
• AutoViML / Auto_TS
  Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ra…
  ☆753Updated 7 months ago
• kearnz / autoimpute
  Python package for Imputation Methods
  ☆248Updated last year
• predict-idlab / tsflex
  Flexible time series feature extraction & processing
  ☆415Updated 7 months ago
• Nixtla / tsfeatures
  Calculates various features from time series data. Python implementation of the R package tsfeatures.
  ☆402Updated 11 months ago
• scikit-learn-contrib / MAPIE
  A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
  ☆1,365Updated this week
• scikit-learn-contrib / boruta_py
  Python implementations of the Boruta all-relevant feature selection method.
  ☆1,561Updated 8 months ago
• manuel-calzolari / sklearn-genetic
  Genetic feature selection module for scikit-learn
  ☆324Updated last year
• tblume1992 / ThymeBoost
  Forecasting with Gradient Boosted Time Series Decomposition
  ☆195Updated last year
• ing-bank / probatus
  SHAP-based validation for linear and tree-based models. Applied to binary, multiclass and regression problems.
  ☆137Updated this week
• cerlymarco / linear-tree
  A python library to build Model Trees with Linear Models at the leaves.
  ☆374Updated 8 months ago
• scikit-learn-contrib / DESlib
  A Python library for dynamic classifier and ensemble selection
  ☆484Updated last year
• alteryx / evalml
  EvalML is an AutoML library written in python.
  ☆805Updated this week
• carlomazzaferro / scikit-hts
  Hierarchical Time Series Forecasting with a familiar API
  ☆224Updated last year
• fabsig / GPBoost
  Combining tree-boosting with Gaussian process and mixed effects models
  ☆598Updated this week
• sktime / sktime-dl
  DEPRECATED, now in sktime - companion package for deep learning based on TensorFlow
  ☆598Updated 8 months ago
• firmai / deltapy
  DeltaPy - Tabular Data Augmentation (by @firmai)
  ☆543Updated last year
• microprediction / timemachines
  Predict time-series with one line of code.
  ☆422Updated 5 months ago
• linkedin / FastTreeSHAP
  Fast SHAP value computation for interpreting tree-based models
  ☆537Updated last year
• zillow / quantile-forest
  Quantile Regression Forests compatible with scikit-learn.
  ☆227Updated this week
• loft-br / xgboost-survival-embeddings
  Improving XGBoost survival analysis with embeddings and debiased estimators
  ☆332Updated 6 months ago
• csinva / imodels
  Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
  ☆1,442Updated last month