A binary decision tree chart in React.
NodeJS Implementation of Decision Tree using ID3 Algorithm.
When building the decision tree you must provide both the training data and the feature names. Do not provide a name for your label column as it is assumed that the last column in the training data represents the labels.
NodeJS implementation of decision tree, random forest, and XGBoost algorithms with comprehensive performance testing (Node.js 20+)
A utility for traversing decision trees by selecting options
A simple class to make statefull decision trees
Get the unscoped, camelCased name of a npm package
Web component - Wizard
Module to easily implement decision tree logic in a react app
Machine learning with functional undertones
A collection of Machine Learning algorithms implemented in Javascript
Module to easily implement decision tree logic in a react app
NodeJS implementation of decision tree using ID3 algorithm
TypeScript Report API for Variant Call Format (VCF) Report Templates
Tools to navigate a graph of questions.
A scikit-learn-inspired machine learning library for Bun/TypeScript.
A lightweight rules engine/decision tree
Node port of the BYU CS 478 machine learning toolkit written in Typescript
React component to display craft ai decision tree
A React component for visualizing decision trees and directed acyclic graphs with multiple layout algorithms, interactive controls, and smooth animations.
TS/JS npm module to train and use a decision tree machine learning model.
auto dj node module for the browser
Simple decision tree / relationship tree component
A self-improving decision tree engine for LLM agents — build, execute, and learn from decision paths. Supports Claude, OpenAI, and Gemini with automatic recommendation caching so agents get faster and cheaper over time. Includes an MCP server for Claude C
MiniBoosts: A collection of boosting algorithms written in Rust 🦀
A library for implementing custom decision trees and random forests
Machine learning framework with spatial modeling, conformal prediction, and gradient boosting competitive with C++
Core traits and abstractions for imbalanced learning in Rust
Ensemble methods for imbalanced learning in Rust - Balanced Random Forest and more
Performance metrics for imbalanced learning in Rust - F1, precision, recall, confusion matrix
Resampling algorithms for imbalanced datasets in Rust - SMOTE, ADASYN, RandomUnderSampler
Visualization engine for legal statutes - decision trees, flowcharts, and dependency graphs
High-performance resampling techniques for imbalanced datasets in Rust
Decision trees in Rust
ID3-based implementation of the M.L. Decision Tree algorithm
ID3-based implementation of the M.L. Decision Tree algorithm
ID3-based implementation of the M.L. Decision Tree algorithm
Uses decisiontrees to generate training examples for gabbler.
RCP Network stands for a neural network that uses decision tree for prediction, struct_rand for generating new data, and readlines and espeak for orating dangling modifiers at random for intentional unintentional humor. This model will be expanded to other reasoning models, like generating small amounts of dictionary data from Duck Duck Go. You can write this Saasagi subroutine automatically through .prewrite. Instructions coming soon. Igrigork: https://github.com/igrigorik/decisiontree Dejan: https://github.com/dejan/espeak-ruby
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