A Julia machine learning framework

Distributed High-Performance Symbolic Regression in Julia

An extensible framework for geospatial data science and geostatistical modeling fully written in Julia

Core functionality for the MLJ machine learning framework

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Fast, scalable and flexible Outlier Detection with Julia

A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Statistical Models with Regularization in Pure Julia

Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks.

TreeParzen.jl, a pure Julia hyperparameter optimiser with MLJ integration

Julia Toolkit with fairness metrics and bias mitigation algorithms

Interpretable Machine Learning via Rule Extraction

A Julia toolbox with resampling methods to correct for class imbalance.

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

An unofficial Julia wrapper for the RAPIDS.ai ecosystem using PythonCall.jl

A pure Julia implementation of the Targeted Minimum Loss-based Estimation

SossMLJ makes it easy to build MLJ machines from user-defined models from the Soss probabilistic programming language

Measures (metrics) for statistics and machine learning

ADRIA: Adaptive Dynamic Reef Intervention Algorithms. A multi-criteria decision support platform for informing reef restoration and adaptation interventions.

A package for wrapping iterative MLJ models in a control strategy

MLJ.jl interface for JLBoost.jl

MLJ.jl interface for JLBoost.jl

Connecting MLJ and MLFlow

A decision-making framework for the cost-efficient design of experiments, balancing the value of acquired experimental evidence and incurred costs.

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A Julia package for modelling Algorithmic Recourse Dynamics.

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Optimally tuned ridge regression when features can be partitioned into groups.

A powerful, no code solution for control and systems engineering

A package with exported learning networks that combine resampling methods from Imbalance.jl and classification models from MLJ

Classification algorithms based on kernel nearest centers

Generalized CP Tensor Decompositions

Utilities to test implementations of the MLJ model interface and provide integration tests for the MLJ ecosystem

A comprehensive QTL analysis tool by multivariate linear mixed model.

One package to train them all

Empirical Bayes shrinkage in Julia

Dynamical systems identification

Invariant Causal Prediction in pure Julia