A collection of research papers on decision, classification and regression trees with implementations.
-
Updated
Mar 16, 2024 - Python
A collection of research papers on decision, classification and regression trees with implementations.
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
A curated list of gradient boosting research papers with implementations.
Tiny Gradient Boosting Tree
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
Machine learning for C# .Net
Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)
Building Decision Trees From Scratch In Python
Performance of various open source GBM implementations
An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.
A predictive model that uses several machine learning algorithms to predict the eligibility of loan applicants based on several factors
Gradient Boosting powered by GPU(NVIDIA CUDA)
An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).
NTUEE Machine Learning, 2017 Spring
Modified XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
This repo uses the Kaggle data set of Melbourne Housing Market. https://www.kaggle.com/anthonypino/melbourne-housing-market
Add a description, image, and links to the gradient-boosting-machine topic page so that developers can more easily learn about it.
To associate your repository with the gradient-boosting-machine topic, visit your repo's landing page and select "manage topics."