mljar-supervised

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

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Builder

mljar

mljar • individual

Stars

3,251

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Forks

431

Using upstream fork count

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Activity Score

0/100

0 commits in 30d

Created

Nov 5, 2018

Project creation date

README Summary

MLJAR Supervised is a Python package that provides automated machine learning (AutoML) capabilities for tabular data. It offers comprehensive features including automated feature engineering, hyperparameter tuning, model explanations, and automatic documentation generation. The package supports multiple algorithms and provides an easy-to-use interface for building, evaluating, and deploying machine learning models.

AI Dev Skills

Unmapped

Tags

Automated Machine Learning (AutoML)Feature EngineeringHyperparameter OptimizationModel EnsemblingCross-validationModel InterpretabilityAutomated Feature SelectionPipeline AutomationModel StackingBayesian OptimizationSelf-hostedHealthcareRetailModel Performance ComparisonAutomated Model SelectionBaseline Model CreationCloud APINo-code/Low-code MLE-commerceManufacturingHyperparameter TuningMulticlass ClassificationAutomated Machine LearningMarketing AnalyticsRegression AnalysisExplainable AIJupyter NotebooksTabularFeature Importance AnalysisInsuranceBinary ClassificationAutomated Report GenerationDemocratized AIFinTechOn-premisePython

Recent Activity

Updated 9 months ago

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30 Days

90 Days

Quality

production

Quality: high
Maturity: production

PM Skills

Scale & ReliabilityDeveloper Platform

Languages

Python100.0%

Timeline

Project created: Nov 5, 2018
Forked: Mar 22, 2026
Your last push: 9 months ago
Upstream last push: 18 days ago
Tracked since: Jul 7, 2025

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