EvalML
EvalML is a free AutoML library for building and evaluating machine-learning pipelines.
What EvalML is
EvalML is a free AutoML library for building and evaluating machine-learning pipelines. It automates pipeline construction and optimization across preprocessing, feature engineering, feature selection, and multiple modeling techniques, with data-quality checks and cross-validation among its listed automation features. Users can choose standard objectives such as mean squared error, cross entropy, and area under the ROC curve, or define domain-specific objectives of their own. Tools for examining models are included. EvalML can also work with Featuretools and Compose in end-to-end supervised machine-learning workflows. Time-series functionality uses Prophet to make predictions from past values, and its development is ongoing. The library is available for Linux, macOS, Windows, API use, and self-hosted setups, and supports Python 3.9–3.11 on the current installation page. It is free, with no free trial. Some dependencies require platform-specific setup: Mac use requires OpenMP for LightGBM, and Apple M1 dependency support is incomplete.
Good
- Free plan with no listed trial requirement
- Automates checks and cross-validation
- Supports custom objective functions
- Includes model introspection tools
Watch out
- Time-series support is still developing
- Mac LightGBM requires OpenMP
- Apple M1 dependency support is incomplete
Who it suits
EvalML suits people who want to automate machine-learning pipeline creation and optimization in code. It may also fit teams combining supervised machine-learning workflows with Featuretools and Compose.
Verdict
EvalML brings pipeline construction, objective selection, and model inspection into a free AutoML library. Check its platform-specific dependency notes before installing, especially on Mac or Apple M1.
The facts
Each one read from EvalML's own pages, with the date it was checked.
What it does
- Add-ons
- Documented add-ons include an update checker and time-series support using Facebook’s Prophet library.checked 2 Oct 2026
- Automation
- The project README lists automation features including data-quality checks and cross-validation.checked 2 Oct 2026
- AutoML objectives
- EvalML supports standard objectives such as mean squared error, cross entropy, and area under the ROC curve, and allows users to define custom objectives.checked 2 Oct 2026
- Custom objectives
- EvalML includes domain-specific objective functions and an interface for defining custom objectives.checked 2 Oct 2026
- End-to-end solutions
- EvalML can be combined with Featuretools and Compose to create end-to-end supervised machine-learning solutions.checked 2 Oct 2026
- End-to-end workflows
- EvalML can be combined with Featuretools and Compose to create end-to-end supervised machine learning solutions.checked 2 Oct 2026
- Example use cases
- Official tutorials cover fraud prediction, lead scoring, cost-benefit objectives, and text data.checked 2 Oct 2026
- Installation
- EvalML can be installed from PyPI, conda-forge, or source, with Python 3.9–3.11 supported on the current installation page.checked 2 Oct 2026
- Intended users
- Alteryx says EvalML can guide people who want to understand how a system works or generate accurate predictions to an efficient solution.checked 2 Oct 2026
- Model understanding
- The user guide includes model-understanding documentation with examples of force plots explaining individual predictions.checked 2 Oct 2026
- Open-source status
- Alteryx describes EvalML as one of its open-source projects and links to its documentation and GitHub project files.checked 2 Oct 2026
- Optional dependencies
- XGBoost and CatBoost support modeling pipelines, while Plotly and ipywidgets support plotting in AutoML searches; these dependencies are optional.checked 2 Oct 2026
- Pipeline construction
- EvalML constructs and optimizes pipelines containing preprocessing, feature engineering, feature selection, and multiple modeling techniques.checked 2 Oct 2026
- Purpose
- EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.checked 2 Oct 2026
- Support
- The project directs users to Stack Overflow for usage questions, GitHub issues for bugs and feature requests, Slack for development discussion, and [email protected] for other questions.checked 2 Oct 2026
- Support and community
- The documentation links users to GitHub, Slack, and Stack Overflow.checked 2 Oct 2026
Where it runs
- Apple M1 caveat
- The documentation says not all dependencies support Apple M1 and recommends installing EvalML with core dependencies on that chip.checked 2 Oct 2026
- Mac limitations
- Running EvalML on Mac requires the OpenMP library for LightGBM, and M1 Macs have incomplete dependency support with core-dependencies installation recommended.checked 2 Oct 2026
- Mac setup caveat
- The documentation says LightGBM requires the OpenMP library on Mac and gives Homebrew instructions for installing it.checked 2 Oct 2026
- Platform limitations
- On Windows, numba and Graphviz may need conda installation and XGBoost may not be pip-installable in some environments.checked 2 Oct 2026
The company
- Founded
- 1997checked 28 Sep 2026
- Headquarters
- Irvine, California, United Stateschecked 28 Sep 2026
- Maker and headquarters
- Alteryx lists its headquarters at 3347 Michelson Drive, Suite 400, Irvine, California 92612.checked 2 Oct 2026
- Maker founding year
- Alteryx says it was founded in 1997.checked 2 Oct 2026
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