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28 search results found

Mljar Supervised ⭐ 2,867

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

Awesome Decision Tree Papers ⭐ 2,266

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

Awesome Gradient Boosting Papers ⭐ 930

A curated list of gradient boosting research papers with implementations.

Hyperparameter_hunter ⭐ 635

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

Ai_competitions ⭐ 539

AI比赛相关信息汇总

Hypergbm ⭐ 306

A full pipeline AutoML tool for tabular data

My Data Competition Experience ⭐ 271

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

Intrusion Detection System Using Machine Learning ⭐ 248

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

Autoquant ⭐ 227

R package for automation of machine learning, forecasting, model evaluation, and model interpretation

Amazon Sagemaker Local Mode ⭐ 220

Amazon SageMaker Local Mode Examples

Aws Machine Learning University Dte ⭐ 177

Machine Learning University: Decision Trees and Ensemble Methods

Benchmarks ⭐ 157

Comparison tools

Ijcai18 Mama Ads Competition ⭐ 68

IJCAI-18 阿里妈妈搜索广告转化预测初赛方案

Auto Flow ⭐ 66

AutoFlow : Automatic machine learning workflow modeling platform

Jlboost.jl ⭐ 61

A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms

Ds Ml Public ⭐ 49

Python Scripts and Jupyter Notebooks

Autotabular ⭐ 45

Automatic machine learning for tabular data. ⚡🔥⚡

Datascienv ⭐ 41

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

Recsys2019 ⭐ 26

The complete code and notebooks used for the ACM Recommender Systems Challenge 2019

Stackgbm ⭐ 24

🌳 Stacked Gradient Boosting Machines

Retrofit ⭐ 15

Automation of feature engineering, machine learning, model evaluation, model interpretation, eda, forecasting, recommender systems and more.

Amex Default Prediction ⭐ 12

Kaggle Competition

Power Usage Predict ⭐ 10

🥉2023 Power Consumption Prediction AI Competition🥉

Credit Risk Management ⭐ 9

A binary classification model is developed to predict the probability of paying back a loan by an applicant. Customer previous loan journey was used to extract useful features using different strategies such as manual and automated feature engineering, and deep learning (CNN, RNN). Various machine learning algorithms such as Boosted algorithms (XGBoost, LightGBM, CatBoost) and Deep Neural Network are used to develop a binary classifier and their performances were compared.

Jobcare Recommendation ⭐ 8

🥉잡케어 추천 알고리즘 경진대회🥉

Predict Delinquency ⭐ 7

🏆신용카드 사용자 연체 예측 AI 경진대회 2등 솔루션🏆

Studentcup2020 ⭐ 5

Cambridgetemperaturenotebooks ⭐ 5

Cambridge UK temperature forecast python notebooks

Related Searches

Python Lightgbm (281)

Jupyter Notebook Lightgbm (266)

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