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Awesome Gradient Boosting Papers ⭐ 930

A curated list of gradient boosting research papers with implementations.

Gpboost ⭐ 486

Combining tree-boosting with Gaussian process and mixed effects models

pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks

Aws Machine Learning University Dte ⭐ 177

Machine Learning University: Decision Trees and Ensemble Methods

Insanely fast Open Source Computer Vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)

Tiny_ml ⭐ 137

numpy 实现的周志华《机器学习》书中的算法及其他一些传统机器学习算法

Opencv Object Tracker Python Sample ⭐ 52

Python版OpenCVのTracking APIのサンプルです。DaSiamRPNアルゴリズムまで対応しています。

sciblox - Easier Data Science and Machine Learning

Error support for Stidler.com / SteamTimeIdler.com

A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

Provably Robust Boosting ⭐ 32

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]

Boostedfactorization ⭐ 31

An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).

Rforestry ⭐ 29

Decision Trees For Ml ⭐ 27

Building Decision Trees From Scratch In Python

Miniboosts ⭐ 25

A collection of boosting algorithms written in Rust 🦀

Disentangled Attribution Curves ⭐ 23

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

Functional gradient boosting based on residual network perception

Spark Ensemble ⭐ 21

Ensemble Learning for Apache Spark 🌲

Machine_learning ⭐ 21

A repository of resources for understanding the concepts of machine learning/deep learning.

Gradient Boosted Normalizing Flows ⭐ 21

We got a stew going!

Tradaboost ⭐ 20

Boosting for transfer learning with single / multiple source(s) Regression / Classification

Rulecovering ⭐ 17

Rule covering for interpretation and boosting

Учебные материалы по курсам связанным с Машинным обучением, которые я читаю в УрФУ. Презентации, блокноты ipynb, ссылки

Statistical Learning Using R ⭐ 16

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Adaboost ⭐ 16

An implementation of the paper "A Short Introduction to Boosting"

Hourbooster ⭐ 15

Simple script to iddle time on steam games without any additional costs.

Two Stage Tradaboost.r2 ⭐ 14

Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).

Hr Analytics ⭐ 12

Analyzing the HR Criteria of a Company and how they promote their Employees and keep Balance between them using Data Analytics, Data Visualizations, and Machine Learning Models for Classification Purposes.

Smartboost.jl ⭐ 12

SMARTboost (boosting of smooth symmetric regression trees)

MILBoost and other boosting algorithms, compatible with scikit-learn

Viola Jones ⭐ 10

A face detection program in python using Viola-Jones algorithm.

Evolinear.jl ⭐ 10

Discord Boost Tool ⭐ 10

Discord Boost Tool 🔥WORKING 2023🔥 Very advanced tool to boost discord servers using token written in python3 boost bot multi discord servers at the same time server booster discord server booster discord server boosting tool discord server boosts discord server booster boost tool server boosts discord server booster discord server booster nitro

Bootcamp Idao 2022 ⭐ 9

IDAO 2022: Machine Learning Bootcamp

In Depth Ml ⭐ 9

In depth machine learning resources

Logitboost ⭐ 9

LogitBoost classification algorithm built on top of scikit-learn

Awesome Deep Learning Finetune Experience ⭐ 8

This repository not only contains experience about parameter finetune, but also other in-practice experience such as model ensemble (boosting, bagging and stacking) in Kaggle or other competitions.

Simple Gradient Boosting ⭐ 8

Very simple and short implementation of gradient boosting in 18 lines of code

Avito Demand Prediction Challenge ⭐ 7

It is a Competition for Regression Challenge held by Kaggle, It is based on a Avito Dataset whose size is 123GB which can be accessed from Kaggle, I have done Data Pre-processing, feature engineering, feature extraction, data visualization, machine learning, stacking and boosting

source code of [Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs](http://proceedings.mlr.press/v108/zantedes

Kaggle House Prices Advanced Regression Techniques ⭐ 7

House Prices: Advanced Regression Techniques - Kaggle competition

Predicting_money_spent_at_resort ⭐ 6

It is From Analytics Vidhya Hackathons, Sponsored by Club Mahindra. It is based on Regression Problem, Where Accuracy matters the most, It is measured by RMSE Score. Different Techniques such as Stacking, Ensembling, Boosting and Scientific Operations such box-cox Operations to reduce skewness of the data.

Titanic Passenger Survival Prediction ⭐ 5

Using Classification Techniques, Data reprocessing, Feature Engineering, Feature Extraction and Classification Algorithms from Machine Learning to Predict who can Survive the attack of Tsunami.

A simplified implement of Adaptive boosting.

Machine_learning_hackathons ⭐ 5

Machine learning and Deep Learning Hackathon Solutions

1-47 of 47 search results

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