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Awesome Decision Tree Papers is a GitHub-hosted open repository that collects research papers on decision trees, classification trees, and regression trees. The repository includes associated code implementations for the papers, and organizes content by publication year, with entries covering 2020, 2021, and 2022. It serves as a resource for machine learning researchers and practitioners.
A curated list of Monte Carlo tree search papers with implementations, organized by conference and journal sources including NeurIPS, ICML, CVPR, ACL, AAAI, and others. The repository also links to similar collections covering graph classification, gradient boosting, and fraud detection papers.
DEAP is an evolutionary computation framework for Python aimed at rapid prototyping and testing of ideas. It provides genetic algorithms over representations such as lists, arrays, sets, dictionaries, trees and Numpy arrays, genetic programming with prefix trees, evolution strategies including CMA-ES, multi-objective optimisation, and co-evolution of multiple populations. It supports parallelisation via multiprocessing and SCOOP and works out of the box with Python 3 since version 0.8.
Decision Tree Classifier and Regressor.
Retext is an ecosystem of plugins that work with natural language as structured data, specifically concrete syntax trees. It is a tool that transforms natural language with plugins, which can inspect and change the natural language. The repository is a monorepo containing packages for parsing English, Dutch and Latin-script prose and serializing syntax trees.
Accord.MachineLearning is a NuGet package, part of the Accord.NET Framework, that contains Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models, and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. Version 3.8.0 targets .NET Standard 1.4 and .NET Framework 3.5.
AIToolbox is a toolbox of AI modules written in Swift, covering graphs and trees, support vector machines, neural networks, PCA, K-Means and genetic algorithms. The framework uses the Accelerate library to speed up computations, except the Linux package versions. A Swift Package is available for Mac and Linux, described as a sub-set of the full framework.
Beautiful Soup is a Python library for pulling data out of HTML and XML files. It provides simple methods and Pythonic idioms for navigating, searching, and modifying a parse tree, and it works with parsers such as lxml and html5lib. The project has been used for screen-scraping work since 2004.
Distributed decision tree ensemble learning in Scala.
CatBoost is an open-source gradient boosting library on decision trees, developed by Yandex as the successor to its MatrixNet algorithm. It natively supports categorical features without preprocessing, offers a fast GPU implementation for large datasets, and reduces overfitting via a proprietary gradient boosting scheme. The library is used for tasks including search, recommendation systems, and weather prediction at Yandex.
Chart.js is an open source JavaScript charting library for the modern web, described as simple yet flexible. It supports eight animated and customisable chart types and renders on HTML5 Canvas across modern browsers, redrawing charts on window resize. Version 4.0 added a colors plugin and tree-shaking to reduce bundle size.
ChefBoost is a lightweight decision tree framework for Python. It supports standard algorithms including ID3, C4.5, CART, CHAID and regression trees, as well as advanced techniques such as gradient boosting, random forest and Adaboost, with support for categorical features.
Code Web Chat is an AI coding tool for VS Code, Cursor, and Antigravity that builds lean markdown prompts from files selected on a file tree. It works with chatbots, APIs, and CLIs, and is described as free, independent, and privacy-first with 100% local operation.
Code2prompt is a command line tool that converts a codebase into a single LLM prompt. It includes source tree representation, prompt templating functionality, and token counting capabilities. Installation options include Cargo, Homebrew, and a pip SDK.
Analyzes structured data and helps produce queries, insights or visualizations.
dtreeviz is a Python library for decision tree visualization and model interpretation. It supports scikit-learn, XGBoost, Spark MLlib, LightGBM and TensorFlow, and uses an adaptor object obtained from dtreeviz.model() to extract model information for visualization.
DVClive is an open-source tool that logs and tracks machine learning metrics, parameters, and models using Git and/or DVC. It is hosted on GitHub as the repository treeverse/dvclive. It provides a simple interface for experiment tracking.
emel is a simple and functional machine learning library built for the Erlang ecosystem and implemented in Gleam. It turns data into functions and supports installing through rebar.config, mix.exs or gleam add. Implemented algorithms include linear regression, k nearest neighbors, decision tree, naive Bayes, k means, perceptron, logistic regression and neural network, along with mathematics, algebra, geometry and statistics.
FATE (Federated AI Technology Enabler) is an industrial grade federated learning open source framework that enables enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation, and provides federated learning algorithms including logistic regression, tree-based algorithms, deep learning and transfer learning.
fractal is a tool for hierarchical agent loops with recursive self-organization, in which autonomous agent loops arrange themselves into a tree of git worktrees and spawn child nodes for separable subtasks. Hard caps on iterations, depth, children, cost and time bound each loop, and run metadata, including cost, is stored in a local SQLite database. Nodes run their agents without permission prompts by default.
Hypernets is a general AutoML framework for building automatic optimization tools for machine learning frameworks and libraries, including tensorflow, keras, pytorch, sklearn, lightgbm and xgboost. It offers an abstract search space representation covering hyperparameter optimization and neural architecture search, with optimization algorithms such as evolution algorithm and Monte Carlo tree search.
JSMLT is an open source JavaScript library for education in machine learning. It implements supervised learning algorithms, including SVM, random forest, decision tree, perceptron, neural networks, k-nearest neighbors and logistic regression, and provides visualization examples for exploring how the algorithms work.
lambda-ml is a small machine learning library written in Lisp, specifically Clojure, aimed at simple, concise implementations of machine learning techniques and utilities. It covers supervised algorithms such as neural networks, decision trees, ensemble methods, k-nearest neighbors, linear and logistic regression, naive Bayes and random forest, plus unsupervised methods including DBSCAN, hierarchical clustering, k-means and non-negative matrix factorization.
MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. It includes tools for converting text to features, classifiers such as Naïve Bayes, Maximum Entropy and Decision Trees, sequence tagging with Hidden Markov Models and Conditional Random Fields, and sampling-based topic model implementations.
