Dependency Packages
-
MLJ.jl1779A Julia machine learning framework
-
MLJBase.jl160Core functionality for the MLJ machine learning framework
-
ConformalPrediction.jl135Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.
-
CounterfactualExplanations.jl117A package for Counterfactual Explanations and Algorithmic Recourse in Julia.
-
OutlierDetection.jl79Fast, scalable and flexible Outlier Detection with Julia
-
MLJTuning.jl67Hyperparameter optimization algorithms for use in the MLJ machine learning framework
-
LaplaceRedux.jl38Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks.
-
TreeParzen.jl35TreeParzen.jl, a pure Julia hyperparameter optimiser with MLJ integration
-
Fairness.jl31Julia Toolkit with fairness metrics and bias mitigation algorithms
-
SIRUS.jl30Interpretable Machine Learning via Rule Extraction
-
Imbalance.jl28A Julia toolbox with resampling methods to correct for class imbalance.
-
ContinuousNormalizingFlows.jl22Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
-
RAPIDS.jl17An unofficial Julia wrapper for the RAPIDS.ai ecosystem using PythonCall.jl
-
TMLE.jl16A pure Julia implementation of the Targeted Minimum Loss-based Estimation
-
SossMLJ.jl15SossMLJ makes it easy to build MLJ machines from user-defined models from the Soss probabilistic programming language
-
ADRIA.jl12ADRIA: Adaptive Dynamic Reef Intervention Algorithms. A multi-criteria decision support platform for informing reef restoration and adaptation interventions.
-
MLJIteration.jl10A package for wrapping iterative MLJ models in a control strategy
-
JLBoostMLJ.jl9MLJ.jl interface for JLBoost.jl
-
MLJJLBoost.jl9MLJ.jl interface for JLBoost.jl
-
MLJFlow.jl8Connecting MLJ and MLFlow
-
CEEDesigns.jl7A decision-making framework for the cost-efficient design of experiments, balancing the value of acquired experimental evidence and incurred costs.
-
MLJParticleSwarmOptimization.jl7-
-
AlgorithmicRecourseDynamics.jl6A Julia package for modelling Algorithmic Recourse Dynamics.
-
Recommenders.jl6-
-
SigmaRidgeRegression.jl5Optimally tuned ridge regression when features can be partitioned into groups.
-
MLJBalancing.jl5A package with exported learning networks that combine resampling methods from Imbalance.jl and classification models from MLJ
-
AutomationLabs.jl5A powerful, no code solution for control and systems engineering
-
EBayes.jl4Empirical Bayes shrinkage in Julia
-
MLJTestIntegration.jl4Utilities to test implementations of the MLJ model interface and provide integration tests for the MLJ ecosystem
-
ModelMiner.jl4One package to train them all
-
AutomationLabsIdentification.jl4Dynamical systems identification
-
MulticlassPerceptron.jl3MulticlassPerceptron.jl
-
InvariantCausalPrediction.jl3Invariant Causal Prediction in pure Julia
-
GenerativeTopographicMapping.jl3A Julia package for Generative Topographic Mapping
-
TMLECLI.jl2-
-
OutlierDetectionData.jl2Easy way to use public outlier detection datasets with Julia
-
OutlierDetectionTest.jl2Test Toolkit for Outlier Detection Algorithms
-
Kinbiont.jl2Ecosystem of numerical methods for microbial kinetics data analysis, from preprocessing to result interpretation.
-
SmartML.jl2-
-
AutomationLabsDepot.jl2Warehouse for dynamical systems identification and control
Loading more...