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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.
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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.
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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.
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Decision Tree Classifier and Regressor.
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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.
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Tree Style Tab is a Firefox extension that organizes tabs as trees. Tabs opened from the current tab become children of that tab, branches can be collapsed by clicking an arrow on the parent tab, and the tree can be restructured by drag and drop. The extension provides an API for other extensions that extend its sidebar behavior.
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AeroSpace is an i3-like tiling window manager for macOS, currently in public beta. It uses a tree paradigm and emulates its own virtual workspaces rather than relying on native macOS Spaces. It offers fast workspace switching without animations and does not require disabling System Integrity Protection. It uses plain text configuration and is CLI first, with multi-monitor support.
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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.
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Broot is a tool for viewing and navigating directory trees. It provides overviews of directories including large ones, enables users to locate directories to change into, and helps maintain awareness of file hierarchy during searches.
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Distributed decision tree ensemble learning in Scala.
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BSPWM is a tiling window manager based on binary space partitioning that represents windows as the leaves of a full binary tree. It responds only to X events and messages received on a dedicated socket, and does not handle keyboard or pointer inputs itself, requiring a third-party program such as sxhkd to translate input events into bspc invocations.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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MeTA is a modern C++ data sciences toolkit. It provides text tokenization including deep semantic features like parse trees, inverted and forward indexes with compression and caching strategies, ranking functions for searching indexes, topic models, classification algorithms, graph algorithms, language models, a CRF implementation for POS-tagging and shallow parsing, and wrappers for liblinear and libsvm.