MLJAR AutoML

What it does

MLJAR AutoML is a Python package for supervised learning on tabular datasets. It prepares and compares candidate models, tunes parameters, builds ensembles and generates explanations and reports. Users provide training data and targets, then evaluate predictions against held-out examples.

The package supports conventional machine-learning algorithms and optional neural or tabular foundation models. Its current quickstart uses a beta release with Python 3.11 or newer; optional model families have additional dependencies and memory requirements. It runs independently of the separate MLJAR Studio notebook application.

What it can do

• Model comparison
• Hyperparameter tuning
• Ensembling
• Feature engineering
• Prediction explanations
• Automatic reports

Before you choose

• The current 1.4 quickstart is a beta release.
• Optional pretrained weights and example datasets have separate licenses.
• Compute and storage costs depend on the chosen models and environment.

Listed under

• Coding
• Sandboxes and infrastructure

Get started

1. Choose a supported release and create a Python virtual environment.
2. Install the package and any needed model extras.
3. Split the data, train models and review validation and test results.

MLJAR AutoML is available under the MIT license. Infrastructure, optional model weights and commercial implementation support have separate requirements.

We couldn’t find published prices for MLJAR AutoML. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 30 Oct 2026. Each source supports part of the overview; availability and prices can change.

• GitHub - mljar/mljar-supervised: Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation · GitHub ↗github.com

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Last checked 30 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.