boosting
There are 385 repositories under boosting topic.
benedekrozemberczki/awesome-gradient-boosting-papers
A curated list of gradient boosting research papers with implementations.
fabsig/GPBoost
Combining tree-boosting with Gaussian process and mixed effects models
dmitryikh/leaves
pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks
aws-samples/aws-machine-learning-university-dte
Machine Learning University: Decision Trees and Ensemble Methods
fengyang95/tiny_ml
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
DoubangoTelecom/compv
Insanely fast Open Source Computer Vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)
Kazuhito00/OpenCV-Object-Tracker-Python-Sample
Python版OpenCVのTracking APIの比較サンプル
m-clark/book-of-models
Spells for everyday living, also a book -- Models Demystified -- now available!
serengil/decision-trees-for-ml
Building Decision Trees From Scratch In Python
fabsig/KTBoost
A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.
rmitsuboshi/miniboosts
A collection of boosting algorithms written in Rust 🦀
Bin-Cao/TrAdaboost
[OPEN teaching project] The transfer learning code for understanding and teaching : Boosting for transfer learning with single / multiple source(s)
danielhanchen/sciblox
sciblox - Easier Data Science and Machine Learning
max-andr/provably-robust-boosting
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]
sharmaroshan/HR-Analytics
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.
Donny-Hikari/Viola-Jones
A face detection program in python using Viola-Jones algorithm.
SteamTimeIdler/stidler
Error support for **idlesteam.com**
SudhakarKuma/Machine_Learning
A repository of resources for understanding the concepts of machine learning/deep learning.
Puneet2000/In-Depth-ML
In depth machine learning resources
benedekrozemberczki/BoostedFactorization
An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).
rz-zhang/PRBoost
The codes for our ACL'22 paper: PRBOOST: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning.
anishsingh20/Statistical-Learning-using-R
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.
anitan0925/ResFGB
Functional gradient boosting based on residual network perception
csinva/disentangled-attribution-curves
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
robert-giaquinto/gradient-boosted-normalizing-flows
We got a stew going!
DrWarpMan/steam-hour-booster
Farm your in-game hours on Steam
pierrenodet/spark-ensemble
Ensemble Learning for Apache Spark 🌲
dayekb/Study
Учебные материалы по курсам связанным с Машинным обучением, которые я читаю в УрФУ. Презентации, блокноты ipynb, ссылки
michikxd/hourbooster
Simple script to iddle time on steam games without any additional costs.
srijarkoroy/adaboost
An implementation of the paper "A Short Introduction to Boosting"
boost-R/FDboost
Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).
sid321axn/Detection_of_Malicious_URLs
In this project, we have detected the malicious URLs using lexical features and boosted machine learning algorithms
isadrtdinov/bootcamp-idao-2022
IDAO 2022: Machine Learning Bootcamp
sibirbil/RuleCovering
Rule covering for interpretation and boosting
ysh329/awesome-deep-learning-finetune-experience
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.