Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

A collection of research papers on decision, classification and regression trees with implementations.

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

A curated list of gradient boosting research papers with implementations.

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

A full pipeline AutoML tool for tabular data

Amazon SageMaker Local Mode Examples

Automatic machine learning for tabular data. ⚡🔥⚡

AutoFlow : Automatic machine learning workflow modeling platform

Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"

hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.

datascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries

Easy Custom Losses for Tree Boosters using Pytorch

TSForecasting: Automated Time Series Forecasting Framework

Simple, opinionated machine learning workflows for rapid iteration.

Assemble an efficient interpretable machine learning workflow.

Kaggle LANL Earthquake Prediction challenge, Genetic Algorithm (DEAP) + CatboostRegressor, private score 2.425 (31 place)

🏆데이콘 AI해커톤 대회 우수상 솔루션🏆