AutoML Frameworks

There is more to an AutoML system than just its performance. This page contains more information about the integrated AutoML frameworks, including links to their papers, repositories , and documentation 📖. Summaries taken directly from the respective documentation pages. Want to integrate your own framework? Adding your own framework is relatively simple.


AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data.

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander Smola
We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file. Unlike existing AutoML frameworks that primarily focus on model/hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in multiple layers. Experiments reveal that our multi-layer combination of many models offers better use of allocated training time than seeking out the best. A second contribution is an extensive evaluation of public and commercial AutoML platforms including TPOT, H2O, AutoWEKA, auto-sklearn, AutoGluon, and Google AutoML Tables. Tests on a suite of 50 classification and regression tasks from Kaggle and the OpenML AutoML Benchmark reveal that AutoGluon is faster, more robust, and much more accurate. We find that AutoGluon often even outperforms the best-in-hindsight combination of all of its competitors. In two popular Kaggle competitions, AutoGluon beat 99% of the participating data scientists after merely 4h of training on the raw data.


Auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction.

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success. In this paper we introduce new AutoML approaches motivated by our winning submission to the second ChaLearn AutoML challenge. We develop PoSH Auto-sklearn, which enables AutoML systems to work well on large datasets under rigid time limits using a new, simple and meta-feature-free meta-learning technique and employs a successful bandit strategy for budget allocation. However, PoSH Auto-sklearn introduces even more ways of running AutoML and might make it harder for users to set it up correctly. Therefore, we also go one step further and study the design space of AutoML itself, proposing a solution towards truly hands-free AutoML. Together, these changes give rise to the next generation of our AutoML system, Auto-sklearn 2.0 . We verify the improvements by these additions in a large experimental study on 39 AutoML benchmark datasets and conclude the paper by comparing to other popular AutoML frameworks and Auto-sklearn 1.0 , reducing the relative error by up to a factor of 4.5, and yielding a performance in 10 minutes that is substantially better than what Auto-sklearn 1.0 achieves within an hour.

Efficient and Robust Automated Machine Learning

Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter
The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. In this work we introduce a robust new AutoML system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub auto-sklearn, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won the first phase of the ongoing ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of auto-sklearn.


FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner.

FLAML: A Fast and Lightweight AutoML Library

Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu
We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of different configurations on the given training data. We investigate the joint impact of multiple factors on both trial cost and model error, and propose several design guidelines. Following them, we build a fast and lightweight library FLAML which optimizes for low computational resource in finding accurate models. FLAML integrates several simple but effective search strategies into an adaptive system. It significantly outperforms top-ranked AutoML libraries on a large open source AutoML benchmark under equal, or sometimes orders of magnitude smaller budget constraints.


GAMA is developed for AutoML research and features a flexible AutoML pipeline, which makes it easy to develop and evaluate new AutoML components. GAMA's benchmarking configuration features evolutionary optimization and ensemble construction.

GAMA: A General Automated Machine Learning Assistant

Pieter Gijsbers, Joaquin Vanschoren
The General Automated Machine learning Assistant (GAMA) is a modular AutoML system developed to empower users to track and control how AutoML algorithms search for optimal machine learning pipelines, and facilitate AutoML research itself. In contrast to current, often black-box systems, GAMA allows users to plug in different AutoML and post-processing techniques, logs and visualizes the search process, and supports easy benchmarking. It currently features three AutoML search algorithms, two model post-processing steps, and is designed to allow for more components to be added.

H2O AutoML

H2O's AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit. H2O offers a number of model explainability methods that apply to AutoML objects (groups of models), as well as individual models (e.g. leader model). Explanations can be generated automatically with a single function call, providing a simple interface to exploring and explaining the AutoML models.

H2O AutoML: Scalable Automatic Machine Learning

Erin LeDell and Sébastien Poirier
H2O is an open source, distributed machine learning platform designed to scale to very large datasets, with APIs in R, Python, Java and Scala. We present H2O AutoML, a highly scalable, fully-automated, supervised learning algorithm which automates the pro- cess of training a large selection of candidate models and stacked ensembles within a single function. The result of the AutoML run is a “leaderboard”: a ranked list of models, all of which can be easily exported for use in a production environment. Models in the leader- board can be ranked by numerous model performance metrics or other model attributes such as training time or average per-row prediction speed. The H2O AutoML algorithm relies on the efficient training of H2O machine learning al- gorithms to produce a large number of models in a short amount of time. H2O AutoML uses a combination of fast random search and stacked ensembles to achieve results competitive with, and often better than, other frameworks which rely on more complex model tuning techniques such as Bayesian optimization or genetic algorithms. H2O AutoML trains a va- riety of algorithms (e.g. GBMs, Random Forests, Deep Neural Networks, GLMs), yielding a healthy amount of diversity across candidate models, which can be exploited by stacked ensembles to produce a powerful final model. The effectiveness of this technique is reflected in the OpenML AutoML Benchmark, which compares the performance of several of the most well known, open source AutoML systems across a number of datasets.


LightAutoML is open-source Python library aimed at automated machine learning. It is designed to be lightweight and efficient for various tasks with tabular, text data.
Paper to be added.
Alexander Ryzhkov, Anton Vakhrushev, Dmitry Simakov, Vasilii Bunakov, Rinchin Damdinov, Alexander Kirilin, Pavel Shvets


The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameters tuning to find the best model 🏆. It is no black-box as you can see exactly how the ML pipeline is constructed (with a detailed Markdown report for each ML model).
No paper available.


TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. It has a focus on optimizing models for biomedical data.

Automating biomedical data science through tree-based pipeline optimization

Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore
Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programing (GP) to recommend an optimized analysis pipeline for the data scientist’s prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data.