jianzhnie / AutoTabular
Automatic machine learning for tabular data. β‘π₯β‘
β68Updated 2 years ago
Related projects β
Alternatives and complementary repositories for AutoTabular
- Benchmark tabular Deep Learning models against each other and other non-DL techniquesβ53Updated 3 years ago
- A neural network hyper parameter tunerβ29Updated 10 months ago
- Revisiting Pretrarining Objectives for Tabular Deep Learningβ62Updated 2 years ago
- Random Forest or XGBoost? It is Time to Explore LCEβ67Updated last year
- Public solution for AutoSeries competitionβ72Updated 4 years ago
- NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for β¦β106Updated 2 years ago
- A toolkit to boost the productivity of machine learning engineers.β52Updated 2 years ago
- Automatically transform all categorical, date-time, NLP variables to numeric in a single line of code for any data set any size.β64Updated 8 months ago
- Kaggle M5 Forecasting - Uncertainty 4th Place Solutionβ31Updated 2 years ago
- Advanced random forest methods in Pythonβ57Updated 11 months ago
- Automated Transparent Genetic Feature Engineeringβ22Updated last year
- XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learningβ32Updated 7 months ago
- Helpers for scikit learnβ16Updated last year
- Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.β41Updated last year
- Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to productionβ¦β29Updated 11 months ago
- ForML - A development framework and MLOps platform for the lifecycle management of data science projectsβ104Updated last year
- β26Updated 5 years ago
- An unsupervised feature selection technique using supervised algorithms such as XGBoostβ88Updated 10 months ago
- Winning solution of the Kaggle "Google Brain - Ventilator Pressure Prediction" competitionβ10Updated 2 years ago
- β28Updated 2 years ago
- Easy Custom Losses for Tree Boosters using Pytorchβ30Updated 3 years ago
- Batch shap calculations.β31Updated last year
- hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating tβ¦β60Updated 3 weeks ago
- machine learning model performance metrics & charts with confidence intervals, optimized with numba to be fastβ16Updated 2 years ago
- CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning systemβ76Updated last year
- A Python library for machine-learning and feedback loops on streaming dataβ59Updated last year
- A library for Time Series EDA (exploratory data analysis)β67Updated 2 months ago
- Smart, automatic detection and stationarization of non-stationary time series data.β29Updated 2 years ago
- β30Updated 2 years ago
- β20Updated 7 months ago