MLJAR AutoML

We’re on our way to revolutionizing the field of machine learning and data analysis. Whether you're a small startup or a large enterprise, MLJAR is here to support you on your machine learning journey.

Data Science Tools

What is AutoML?

The most advanced Machine Learning tool with the simplest User Interface

A The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It’s a complete package that abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameters tuning to find the best model.-of-the-art tool designed for Data Science professionals. Our platform provides advanced automated machine learning capabilities, enabling effective model creation and data analysis process optimization.

Open-source MIT license

The Over 100 citations in research papers

More than 1 million downloads

Works with Python with versions 3.8, 3.9, 3.10, 3.11

Uniqe features: automated documentation, fairness metric

Variety of algorithms as: ensemble, stacked ensemble, XGBoost, CatBoost and more

A few steps to achieve your goals!

Define your goals

Clearly outline your analytics goals while specifying how machine learning algorithms can contribute to achieving those objectives.

Provide a good data set & implement MLJAR

Prepare your data for machine learning. Valuable data set is the key to achieve profitable results. Then install mljar-supervised, and let the magic begin!

Deploy models and visualize

Deploy your results or use Mercury. Use the wide visualize libraries to make your work attractive for non-technical users.

Work is done! Coffee time :)

AutoML steps
1M

Downloads

3K

Stars

300

Forks

5K

Users

Automate all stages of creating a machine learning pipeline, and explore how our features work:

Features preprocessing

Features selection

Algorithm selection

Golden features

Models ensembling

ML explainability

Features

Discover the unique features that make our AutoML the state of the art. Find those that make your ML project advanced, super easy, and understandable.

Complete pipeline

Simplifies the entire machine learning process from data preprocessing to model deployment.

Golden feature

Efficiently pinpoints the most influential variables for optimal model performance.

Model leaderboard

Enables easy comparison and selection of models based on performance metrics.

Automated reports

The report from running AutoML will contain the table with information about each model score and the time needed to train the model.

Feature selection

MLJAR AutoML takes care of features preprocessing like missing values imputation and converting categoricals, it can also handle target values preprocessing.

Auto-saving models

All models in the AutoML are saved and loaded automatically. No need to call save() or load().

Hyperparameter tuning

Optimizes model performance by automatically searching for the best combination of hyperparameters, saving time and effort.

Variety of algorithms

Choose from many algorithms such as: XGBoost, CatBoost, Neural Networks, Decision trees, Random forest and many more...

One AutoML for all your needs

Get results quickly

Ideal for initial data analysis

Results in few minues

ML Explanations

Production ready ML

Balance between performance and speed

Time-constrained prediction model search

Deploy models quickly

Best quality models

Wide range of ML algorithms

Advanced feature engineering

Automated documentation

Highly tuned ML

Utilize Optuna framework

Squeeze model top performance

Use simple Python API

Discover more

Find details on our GitHub and in our documentation.