htmlparser2 tree adapter for parse5.
mdast utility to serialize markdown
mdast utility to parse markdown
unist utility to visit nodes
mdast extension to parse and serialize GFM task list items
TypeScript definitions for @babel/generator
Color helpers to ease transformation between formats, gamut, etc
unist utility to serialize a node, position, or point as a human readable location
unist utility to recursively walk over nodes, with ancestral information
TypeScript definitions for @babel/traverse
mdast extension to parse and serialize GFM (GitHub Flavored Markdown)
TypeScript definitions for unist
mdast extension to parse and serialize MDX (or MDX.js) expressions
hast utility to transform to preact, react, solid, svelte, vue, etc
mdast extension to parse and serialize GFM strikethrough
hast utility to create trees
Regular Expressions parser in JavaScript
mdast extension to parse and serialize MDX.js ESM (import/exports)
unist utility to get the position of a node
mdast utility to transform to hast
mdast extension to parse and serialize GFM tables
TypeScript definitions for hast
hast utility to create an element from a simple CSS selector
mdast extension to parse and serialize MDX or MDX.js JSX
Rumale::Tree provides classifier and regression based on decision tree algorithms with Rumale interface.
Rumale::Ensemble provides ensemble learning algorithms, such as AdaBoost, Gradient Tree Boosting, and Random Forest, with Rumale interface.
Rumale is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Support Vector Machine, Logistic Regression, Ridge, Lasso, Multi-layer Perceptron, Naive Bayes, Decision Tree, Gradient Tree Boosting, Random Forest, K-Means, Gaussian Mixture Model, DBSCAN, Spectral Clustering, Mutidimensional Scaling, t-SNE, Fisher Discriminant Analysis, Neighbourhood Component Analysis, Principal Component Analysis, Non-negative Matrix Factorization, and many other algorithms.
SVMKit is a machine learninig library in Ruby. SVMKit provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. SVMKit supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Random Forest, K-nearest neighbor algorithm, K-Means, DBSCAN, Principal Component Analysis, and Non-negative Matrix Factorization. Note that the SVMKit has been deprecated and has been renamed to Rumale.