Core functionality for the MLJ machine learning framework
-
Updated
May 8, 2024 - Julia
Core functionality for the MLJ machine learning framework
Home of the MLJ model registry and tools for model queries and mode code loading
A set of tutorials to show how to use Julia for data science (DataFrames, MLJ, ...)
Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)
Julia Toolkit with fairness metrics and bias mitigation algorithms
Parallel & lightning fast implementation of available classic and contemporary variants of the KMeans clustering algorithm
Hyperparameter optimization algorithms for use in the MLJ machine learning framework
An API for dispatching on the "scientific" type of data instead of the machine type
An Introduction to Artificial Intelligence with Julia
Repository implementing MLJ interface for MultivariateStats models.
MLJ.jl interface for GLM.jl models
A Least Squares Support Vector Machine implementation in pure Julia
SossMLJ makes it easy to build MLJ machines from user-defined models from the Soss probabilistic programming language
Binary Classification applying dimensionality reduction and hyperparameter tunning, working on MLJ framework in Julia. The Data comes from a Sonar System
One package to train them all
Julia learning resources collected from various Julia Computing repos!
Repository housing feature selection algorithms for use with the machine learning toolbox MLJ.
Connecting MLJ and MLFlow
(now superseded by MLJLinearModels)
Add a description, image, and links to the mlj topic page so that developers can more easily learn about it.
To associate your repository with the mlj topic, visit your repo's landing page and select "manage topics."