catboost / tutorials

Related projects ⓘ

Alternatives and complementary repositories for tutorials

• maks-sh / scikit-uplift
  uplift modeling in scikit-learn style in python
  ☆732Updated last year
• sberbank-ai-lab / LightAutoML
  LAMA - automatic model creation framework
  ☆901Updated 2 years ago
• SauceCat / PDPbox
  python partial dependence plot toolbox
  ☆845Updated 2 months ago
• feature-engine / feature_engine
  Feature engineering package with sklearn like functionality
  ☆1,913Updated this week
• rushter / heamy
  A set of useful tools for competitive data science.
  ☆552Updated last year
• vecxoz / vecstack
  Python package for stacking (machine learning technique)
  ☆685Updated 2 months ago
• scikit-learn-contrib / boruta_py
  Python implementations of the Boruta all-relevant feature selection method.
  ☆1,509Updated 2 months ago
• stanfordmlgroup / ngboost
  Natural Gradient Boosting for Probabilistic Prediction
  ☆1,654Updated last week
• scikit-learn-contrib / category_encoders
  A library of sklearn compatible categorical variable encoders
  ☆2,410Updated last month
• h2oai / pystacknet
  ☆286Updated 2 years ago
• hyperopt / hyperopt-sklearn
  Hyper-parameter optimization for sklearn
  ☆1,587Updated 4 months ago
• catboost / catboost
  A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other ma…
  ☆8,077Updated this week
• aimclub / FEDOT
  Automated modeling and machine learning framework FEDOT
  ☆643Updated this week
• aerdem4 / lofo-importance
  Leave One Feature Out Importance
  ☆817Updated 9 months ago
• alteryx / evalml
  EvalML is an AutoML library written in python.
  ☆774Updated this week
• limexp / xgbfir
  XGBoost Feature Interactions Reshaped
  ☆426Updated 6 years ago
• parrt / random-forest-importances
  Code to compute permutation and drop-column importances in Python scikit-learn models
  ☆600Updated last month
• TeamHG-Memex / eli5
  A library for debugging/inspecting machine learning classifiers and explaining their predictions
  ☆2,758Updated 2 years ago
• cerlymarco / shap-hypetune
  A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
  ☆566Updated 5 months ago
• DenisVorotyntsev / CategoricalEncodingBenchmark
  Benchmarking different approaches for categorical encoding for tabular data
  ☆171Updated 2 years ago
• Far0n / kaggletils
  ☆382Updated 7 years ago
• flennerhag / mlens
  ML-Ensemble – high performance ensemble learning
  ☆846Updated 11 months ago
• kaz-Anova / StackNet
  StackNet is a computational, scalable and analytical Meta modelling framework
  ☆1,322Updated 6 years ago
• cod3licious / autofeat
  Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
  ☆499Updated last month
• AxeldeRomblay / MLBox
  MLBox is a powerful Automated Machine Learning python library.
  ☆1,500Updated last year
• abhayspawar / featexp
  Feature exploration for supervised learning
  ☆763Updated 3 years ago
• AutoViML / Auto_ViML
  Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upo…
  ☆522Updated 4 months ago
• MLWave / Kaggle-Ensemble-Guide
  Code for the Kaggle Ensembling Guide Article on MLWave
  ☆1,550Updated 6 years ago
• jeongyoonlee / Kaggler
  Code for Kaggle Data Science Competitions
  ☆743Updated 6 months ago
• Ekeany / Boruta-Shap
  A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.
  ☆582Updated 8 months ago