A Julia machine learning framework

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.

Home of the MLJ model registry and tools for model queries and mode code loading

Fast, scalable and flexible Outlier Detection with Julia

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

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.

Julia implementation of Modal Decision Trees & Forests, for interpretable classification of spatial and temporal data. Long live Symbolic Learning!!

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

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

Repository implementing MLJ interface for MultivariateStats models.

A Julia package for modelling Algorithmic Recourse Dynamics.

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

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

A powerful, no code solution for control and systems engineering

One package to train them all

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

Dynamical systems identification

Empirical Bayes shrinkage in Julia

Invariant Causal Prediction in pure Julia

MulticlassPerceptron.jl

A Julia package for Generative Topographic Mapping

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