MLJAR AutoML · Python

Build machine learning models from your tabular data¶

Train and compare models, read their reports, and save the results for future predictions. Start with a Python script or use a conversational notebook in MLJAR Studio.

Find your first example Installation guide

AutoML leaderboard¶

Customer churn · validation results
Model	Log loss ↓
Baseline	0.433171
Decision Tree	0.312209
Linear	0.232629
LightGBM	0.134072
Ensemble Best	0.132638

Choose your starting point¶

Start in Python¶

Follow a standalone script or notebook from data loading to evaluation and saved predictions. The beginner examples use standard CPU models.

Run your first tutorial

Use MLJAR Studio¶

Work in a conversational notebook with visible Python code. Use the tutorial's notebook as the starting point and ask for explanations as you go.

Explore MLJAR Studio · Get a tutorial notebook

The Python examples are tested; their Studio walkthroughs are still being validated.

Learn with a real question¶

Start here · Engineering · Regression

Predict a number from a CSV¶

Estimate concrete strength from its ingredients and age. Compare prediction errors in MPa, read the report, and reload your saved model.

Follow the concrete-strength tutorial →

Start here · Business · Classification

Predict which customers may leave¶

Estimate churn probabilities from customer usage. Learn about missed customers, false alarms, and the difference between a probability and a decision.

Follow the customer-churn tutorial →

Browse examples and advanced guides

What you can do with AutoML¶

Read automatic reports¶

Compare models in the leaderboard and inspect their settings, evaluation, and available explanations.

Read a report

Generate features¶

Try golden features and feature discovery, then check whether they improve validation results. Discovery is included in the base installation.

Explore feature discovery

Try foundation models¶

Use optional Kumo and TabICLv2 tabular foundation models. Check dependencies, first-use weight downloads, and hardware requirements.

Explore TFMs

Evaluate fairness¶

Measure differences between groups and assess bias mitigation alongside predictive performance. The choice of groups and metrics matters.

Explore fairness and mitigation

Export a prediction app¶

Create a web interface for individual rows and CSV files. Export files from the base package; add the optional runtime to run the app.

Create an app

Customize your workflow¶

Choose a training mode, define an evaluation metric, or supply validation splits that reflect your data's groups and timing.

Find advanced guides

Installation¶

In a Python 3.11+ environment, install the exact beta used by the new tutorials:

python -m pip install 'mljar-supervised==1.4.0b1'

The beginner workflows were tested on Python 3.11 and 3.12. See the installation guide for environment setup, supported platforms, and optional neural models, TFMs, and app dependencies.

Basic usage¶

Download customer_churn.py into your working folder, then run:

python customer_churn.py --output AutoML_CustomerChurn

The script downloads the reviewed data, trains models, evaluates held-out rows, and verifies predictions after reload. Open AutoML_CustomerChurn/automl/README.html to read your report. Follow the complete explanation.

Keep going¶

Save and reload models · API reference · Get help · Contribute · Source and license