Alternatives and similar repositories for autoxgb:

Users that are interested in autoxgb are comparing it to the libraries listed below

• Ekeany / Boruta-Shap
  A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.
  ☆613Updated last year
• cerlymarco / shap-hypetune
  A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
  ☆577Updated 10 months ago
• StatMixedML / XGBoostLSS
  An extension of XGBoost to probabilistic modelling
  ☆593Updated 9 months 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
• 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…
  ☆752Updated 7 months ago
• AutoViML / Auto_ViML
  Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upo…
  ☆535Updated 2 months ago
• aerdem4 / lofo-importance
  Leave One Feature Out Importance
  ☆832Updated 2 months ago
• ThomasBury / arfs
  All Relevant Feature Selection
  ☆132Updated last week
• linkedin / FastTreeSHAP
  Fast SHAP value computation for interpreting tree-based models
  ☆537Updated last year
• StatMixedML / LightGBMLSS
  An extension of LightGBM to probabilistic modelling
  ☆302Updated 10 months ago
• predict-idlab / powershap
  A power-full Shapley feature selection method.
  ☆204Updated 11 months ago
• tusharsarkar3 / XBNet
  Boosted neural network for tabular data
  ☆213Updated 8 months ago
• arnabbiswas1 / kaggle_pipeline_tps_aug_22
  Kaggle Pipeline for tabular data competitions
  ☆204Updated 9 months ago
• atif-hassan / PyImpetus
  PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in…
  ☆133Updated last month
• loft-br / xgboost-survival-embeddings
  Improving XGBoost survival analysis with embeddings and debiased estimators
  ☆332Updated 6 months ago
• cod3licious / autofeat
  Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
  ☆513Updated 3 weeks ago
• AnotherSamWilson / miceforest
  Multiple Imputation with LightGBM in Python
  ☆375Updated 8 months ago
• feature-engine / feature_engine
  Feature engineering package with sklearn like functionality
  ☆2,028Updated 3 weeks ago
• baosenguo / Kaggle-MoA-2nd-Place-Solution
  ☆167Updated 4 years ago
• kearnz / autoimpute
  Python package for Imputation Methods
  ☆248Updated last year
• atif-hassan / FRUFS
  An unsupervised feature selection technique using supervised algorithms such as XGBoost
  ☆89Updated last year
• ryancheunggit / tabular_dae
  ☆52Updated 2 years ago
• rasgointelligence / feature-engineering-tutorials
  Data Science Feature Engineering and Selection Tutorials
  ☆284Updated 2 months ago
• csinva / imodels
  Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
  ☆1,442Updated last month
• smazzanti / mrmr
  mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.
  ☆581Updated 4 months ago
• alteryx / evalml
  EvalML is an AutoML library written in python.
  ☆805Updated this week
• lmassaron / deep_learning_for_tabular_data
  A presention of core concepts and a data generator making easier using tabular data with TensorFlow and Keras
  ☆43Updated 2 years ago
• mikekeith52 / scalecast
  The practitioner's forecasting library
  ☆334Updated 2 weeks ago
• tunguz / TabularBenchmarks
  ☆199Updated last week
• scikit-learn-contrib / boruta_py
  Python implementations of the Boruta all-relevant feature selection method.
  ☆1,561Updated 8 months ago