BetaML
BetaML: The Beta Machine Learning Toolkit, a self-contained repository of Machine Learning algorithms in Julia - Published in JOSS (2021)
CVtreeMLE
CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R - Published in JOSS (2023)
proglearn
NeuroData's package for exploring and using progressive learning algorithms
sdtf
Exploring streaming options for decision trees and random forests. Based on scikit-learn fork.
treeple
Scikit-learn compatible decision trees beyond those offered in scikit-learn
lightgbm
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
lleaves
Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
ModalDecisionTrees
Julia implementation of Modal Decision Trees & Forests, for interpretable classification of spatial and temporal data. Long live Symbolic Learning!!
yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
pyscipopt-ml
Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
dtreeviz
A python library for decision tree visualization and model interpretation.
pynets
A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning
rgf
Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.
explore
R package that makes basic data exploration radically simple (interactive data exploration, reproducible data science)
https://github.com/erictleung/ml-final-proj
:wine_glass: CS559/659 Machine Learning Final Project on Predicting Wine Quality
metrics_for_individual_tree_mortality_models
How performance metric choice influences individual tree mortality model selection - code and data
https://github.com/atharvapathak/telecom_churn_case_study
Build a classification model for reducing the churn rate for a telecom company
robusttrees
[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
https://github.com/cn-tu/machine-learning-in-ebpf
This repository contains the code for the paper "A flow-based IDS using Machine Learning in eBPF", Contact: Maximilian Bachl
stochtree
Stochastic tree ensembles (BART / XBART) for supervised learning and causal inference
io.github.andrewquijano:level-site-ppdt
Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees
ml_individual_tree_mortality
Does Machine Learning outperform Logistic Regression in predicting individual tree mortality? - code and data
df-dn-paper
Conceptual & empirical comparisons between decision forests & deep networks