There are 2 repositories under gradient-boosted-trees topic.
A curated list of gradient boosting research papers with implementations.
Represent trained machine learning models as Pyomo optimization formulations
Hybrid model of Gradient Boosting Trees and Logistic Regression (GBDT+LR) on Spark
A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)
A self-generalizing gradient boosting machine which doesn't need hyperparameter optimization
Multiobjective black-box optimization using gradient-boosted trees
An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.
Influence Estimation for Gradient-Boosted Decision Trees
Gradient boosting for OCaml using the R xgboost package under the carpet
OncoNetExplainer: Explainable Prediction of Cancer Types Based on Gene Expression Data
Analysis of information about startup companies done using machine learning and data analytics methods to predict the success of the startup companies.
Sequential skip prediction using deep learning and ensembles
PhishyAI trains ML models for Phishy, a Gmail extension which leverages ML to detect phishing attempts in all incoming emails
Machine learning multiclassification task in particle physics experiment (Belle II) with deep neural networks (DNN) and gradient boosted decision trees (XGBoost).
Predicting the daily sales of Rossmann Stores
Binary text difficulty classification with tf-idf, word2vec, and other linguistic features with multinomial naive bayes, logistic regression, and gradient boosted decision trees.
An implementation of the algorithms from the camera-ready version of the paper "Coresets for Decision Trees of Signals" (NeurIPS'2021) by Ibrahim Jubran, Ernesto Evgeniy Sanches Shayda, Ilan Newman, and Dan Feldman.
Analisis Prediktif Capaian Indikator Utama Pembangunan di Indonesia
Gained insights into the New York City Airbnb rental properties and concluded the neighbourhoods with most attractive Airbnb rentals and the type of rental properties with most reviews. Furthermore, concluded the economic viability of the rentals with missing reviews through machine learning models such as k-NN, decision tree and gradient boosted tree (GBT) classifiers implemented via data science platform RapidMiner.
Machine learning project comparing several algorithms to predict the outcome of shelter animals. Based on the former Kaggle competition: https://www.kaggle.com/c/shelter-animal-outcomes.
Iris Species Classification usin various ML models.
Projects for ECE 475 - Freq. Machine Learning
In this project I wanted to predict attrition based on employee data. The data is an artificial dataset from IBM data scientists. It contains data for 1470 employees. Te dataset contains the following information per employee:
sentiment analysis using the movie reviews from the imdb database
Predict house prices in Pasig City, Philippines using TensorFlow Decision Forests
A Python Package for a Sparse Additive Boosting Regressor
A Jupyter Notebook explaining the Gradient Tree Boosting algorithm [German]
Fraud detection on mobile banking transactions
Developed a model/Spark ML pipeline stream to identify potential customers that may purchase top up services in the future.
Different feature selection methods like SVD, SelectKBest, RFE are used to choose best features, then different machine learning algorithms like Random Forest, Gradient Boosting Tree, XGBoost together with GridsearchCV etc are applied and compared to choose the good model which is the best fit for the dataset.
Few simple implementations of common ML algorithms from scratch (numpy)
Basic implementation of Gradient Boosting Trees