Nimbus
Nimbus: a Ruby gem to implement Random Forest algorithms in a genomic selection context - Published in JOSS (2017)
quantile-forest
quantile-forest: A Python Package for Quantile Regression Forests - Published in JOSS (2024)
BetaML
BetaML: The Beta Machine Learning Toolkit, a self-contained repository of Machine Learning algorithms in Julia - Published in JOSS (2021)
rrcf
rrcf: Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams - Published in JOSS (2019)
flaml
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
smac
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
emlearn
Machine Learning inference engine for Microcontrollers and Embedded devices
tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
treeple
Scikit-learn compatible decision trees beyond those offered in scikit-learn
random-survival-forest
A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.
https://github.com/epistasislab/tpot2
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
comparative_ml_analysis_bioinformatics
A comprehensive analysis of gene expression data using machine learning techniques in Python and R, focusing on predictive modeling and data visualization
mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
h2o
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
metasklearn
MetaSklearn: A Metaheuristic-Powered Hyperparameter Optimization Framework for Scikit-Learn Models.
aorsf
aorsf: An R package for supervised learning using the oblique random survival forest - Published in JOSS (2022)
dtreeviz
A python library for decision tree visualization and model interpretation.
edarf
edarf: Exploratory Data Analysis using Random Forests - Published in JOSS (2016)
randomForestExplainer
A set of tools to understand what is happening inside a Random Forest
arboreto
A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.
https://github.com/csinva/disentangled-attribution-curves
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
https://github.com/blasbenito/spatialrf
R package to fit spatial models with Random Forest
plncpro
A machine learning model for the prediction of lncRNAs (Singh et. al NAR 2017)
https://github.com/arogozhnikov/infiniteboost
InfiniteBoost: building infinite ensembles with gradient descent
rfUtilities
R package for random forests model selection, inference, evaluation and validation
tree.interpreter
Decision tree interpreter for randomForest/ranger as described in
metrics_for_individual_tree_mortality_models
How performance metric choice influences individual tree mortality model selection - code and data
imesc
This app is intended to dynamically integrate machine learning techniques to explore multivariate data sets.
https://github.com/cn-tu/ids-backdoor
Contact: Maximilian Bachl, Alexander Hartl. Explores defenses against backdoors and poisoning attacks for Intrusion Detection Systems. Code for "EagerNet" is in the "eager" branch.
fraud-detection-transaction-data
Pipeline for analyzing fraud in card transaction data-sets with an addition of graph features, modeled using Random Forest
breast_cancer_diagnosis_ml
This project demonstrates the use of machine learning models to predict breast cancer diagnoses. The repository covers the entire workflow from data preprocessing and feature engineering to model training and evaluation, providing insights into diagnosis prediction with various ML models.
https://github.com/atharvapathak/telecom_churn_case_study
Build a classification model for reducing the churn rate for a telecom company
bayesian-hyper-parameter-optimization-for-malware-detection
AI-CyberSec 2021 Workshop CEUR Publication(AI-2021 Forty-first SGAI International Conference)
https://github.com/comp-physics/quantum-hrf-tomography
Reconstructing real-valued quantum states using Hadamard Random Forest (HRF) tomography
ml_individual_tree_mortality
Does Machine Learning outperform Logistic Regression in predicting individual tree mortality? - code and data
workshop-random-forests
UNSW codeRs workshop: Introduction to Classification Trees and Random Forests in R. These documents will walk you through examples to fit classification trees and random forest models in R.
prophitbet-soccer-bets-predictor
ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.
pye-plus
Multi-Criteria Decision Making (MCDM) Framework for Building Energy Systems with Expedited Computation using Machine Learning (ML) Techniques
https://github.com/cmcc-foundation/cmcc-hybrid-estuaryboxmodel
CMCC-Hybrid-EBM collects all the experiments releated to the estimation of salt-wedge intrusion length and salinity concentration using hybrid and machine learning based approaches.
https://github.com/amr-yasser226/intrusion-detection-kaggle
End-to-end pipeline for multi-class cyber-attack detection using per-flow network features: data profiling, deduplication, skew-correction, outlier treatment, feature engineering, imbalance handling, and tree-based modeling (XGBoost, LightGBM, CatBoost, stacking), with a final Kaggle submission scoring 0.9146 public / 0.9163 private.
trump-speech-analysis
Statistical patterns in political rhetoric: The quantitative analysis of Donald Trump's 2024 election campaign speeches
https://github.com/cjabradshaw/aussoilhg
Predicting continental distribution of soil mercury concentration in Australia