EvalML

Spec sheet

What it does

EvalML is an AutoML library that builds, optimizes, and evaluates machine-learning pipelines using domain-specific objective functions.Read 2 Oct 2026 · evalml.alteryx.com

Purpose

EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.Read 2 Oct 2026 · evalml.alteryx.com

Intended users

Alteryx says EvalML can guide people who want to understand how a system works or generate accurate predictions to an efficient solution.Read 2 Oct 2026 · alteryx.com

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.Read 2 Oct 2026 · github.com

Headquarters

Irvine, California, United StatesRead 28 Sep 2026 · evalml.alteryx.com

Founded

1997Read 28 Sep 2026 · evalml.alteryx.com

Add-ons

Documented add-ons include an update checker and time-series support using Facebook’s Prophet library.Read 2 Oct 2026 · evalml.alteryx.com

Apple M1 caveat

The documentation says not all dependencies support Apple M1 and recommends installing EvalML with core dependencies on that chip.Read 2 Oct 2026 · evalml.alteryx.com

Automation

The project README lists automation features including data-quality checks and cross-validation.Read 2 Oct 2026 · github.com

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.Read 2 Oct 2026 · evalml.alteryx.com

Custom objectives

EvalML includes domain-specific objective functions and an interface for defining custom objectives.Read 2 Oct 2026 · github.com

End-to-end solutions

EvalML can be combined with Featuretools and Compose to create end-to-end supervised machine-learning solutions.Read 2 Oct 2026 · evalml.alteryx.com

End-to-end workflows

EvalML can be combined with Featuretools and Compose to create end-to-end supervised machine learning solutions.Read 2 Oct 2026 · evalml.alteryx.com

Example use cases

Official tutorials cover fraud prediction, lead scoring, cost-benefit objectives, and text data.Read 2 Oct 2026 · evalml.alteryx.com

Installation

EvalML can be installed from PyPI, conda-forge, or source, with Python 3.9–3.11 supported on the current installation page.Read 2 Oct 2026 · evalml.alteryx.com

Mac limitations

Running EvalML on Mac requires the OpenMP library for LightGBM, and M1 Macs have incomplete dependency support with core-dependencies installation recommended.Read 2 Oct 2026 · evalml.alteryx.com

Mac setup caveat

The documentation says LightGBM requires the OpenMP library on Mac and gives Homebrew instructions for installing it.Read 2 Oct 2026 · evalml.alteryx.com

Maker and headquarters

Alteryx lists its headquarters at 3347 Michelson Drive, Suite 400, Irvine, California 92612.Read 2 Oct 2026 · alteryx.com

Maker founding year

Alteryx says it was founded in 1997.Read 2 Oct 2026 · alteryx.com

Model understanding

The user guide includes model-understanding documentation with examples of force plots explaining individual predictions.Read 2 Oct 2026 · evalml.alteryx.com

Open-source status

Alteryx describes EvalML as one of its open-source projects and links to its documentation and GitHub project files.Read 2 Oct 2026 · alteryx.com

Optional dependencies

XGBoost and CatBoost support modeling pipelines, while Plotly and ipywidgets support plotting in AutoML searches; these dependencies are optional.Read 2 Oct 2026 · evalml.alteryx.com

Pipeline construction

EvalML constructs and optimizes pipelines containing preprocessing, feature engineering, feature selection, and multiple modeling techniques.Read 2 Oct 2026 · github.com

Platform limitations

On Windows, numba and Graphviz may need conda installation and XGBoost may not be pip-installable in some environments.Read 2 Oct 2026 · evalml.alteryx.com

Support and community

The documentation links users to GitHub, Slack, and Stack Overflow.Read 2 Oct 2026 · evalml.alteryx.com

Time series

EvalML includes time-series functionality for using past values to predict future values, and its documentation says that support is still being actively developed.Read 2 Oct 2026 · evalml.alteryx.com

Time-series add-on

Time-series support uses Facebook’s Prophet library, installed with the prophet extra.Read 2 Oct 2026 · evalml.alteryx.com

Windows setup caveat

For Windows pip installs, the documentation recommends installing numba first for SHAP and prediction explanations, and python-graphviz for plotting utilities.Read 2 Oct 2026 · evalml.alteryx.com

Where Specifiction places it

• AutoML Software#13 of 307.8