EvalML

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About

EvalML is ranked #2 of 28 in AutoML software on Inferse. It runs on API, Linux, macOS, Self-hosted, Windows.

Compared on AutoML software

Free plan: Yesevalml.alteryx.com
Feature engineering: Yesevalml.alteryx.com
Automated model selection: Yesevalml.alteryx.com
Model explainability: Yesevalml.alteryx.com
Workflow interface: codeevalml.alteryx.com
Hosting model: self_hostedevalml.alteryx.com

Facts

What it does: EvalML is an AutoML library that builds, optimizes, and evaluates machine-learning pipelines using domain-specific objective functions.evalml.alteryx.com · 2 Oct 2026
End-to-end solutions: EvalML can be combined with Featuretools and Compose to create end-to-end supervised machine-learning solutions.evalml.alteryx.com · 2 Oct 2026
Automation: The project README lists automation features including data-quality checks and cross-validation.github.com · 2 Oct 2026
Pipeline construction: EvalML constructs and optimizes pipelines containing preprocessing, feature engineering, feature selection, and multiple modeling techniques.github.com · 2 Oct 2026
Model understanding: EvalML provides tools to understand and introspect models.github.com · 2 Oct 2026
Custom objectives: EvalML includes domain-specific objective functions and an interface for defining custom objectives.github.com · 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.evalml.alteryx.com · 2 Oct 2026
Optional dependencies: XGBoost and CatBoost support modeling pipelines, while Plotly and ipywidgets support plotting in AutoML searches; these dependencies are optional.evalml.alteryx.com · 2 Oct 2026
Time-series add-on: Time-series support uses Facebook’s Prophet library, installed with the prophet extra.evalml.alteryx.com · 2 Oct 2026
Platform limitations: On Windows, numba and Graphviz may need conda installation and XGBoost may not be pip-installable in some environments.evalml.alteryx.com · 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.evalml.alteryx.com · 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.github.com · 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.alteryx.com · 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.alteryx.com · 2 Oct 2026
Purpose: EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.evalml.alteryx.com · 2 Oct 2026
End-to-end workflows: EvalML can be combined with Featuretools and Compose to create end-to-end supervised machine learning solutions.evalml.alteryx.com · 2 Oct 2026
Add-ons: Documented add-ons include an update checker and time-series support using Facebook’s Prophet library.evalml.alteryx.com · 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.evalml.alteryx.com · 2 Oct 2026
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.evalml.alteryx.com · 2 Oct 2026
Model understanding: The user guide includes model-understanding documentation with examples of force plots explaining individual predictions.evalml.alteryx.com · 2 Oct 2026
Example use cases: Official tutorials cover fraud prediction, lead scoring, cost-benefit objectives, and text data.evalml.alteryx.com · 2 Oct 2026
Windows setup caveat: For Windows pip installs, the documentation recommends installing numba first for SHAP and prediction explanations, and python-graphviz for plotting utilities.evalml.alteryx.com · 2 Oct 2026
Mac setup caveat: The documentation says LightGBM requires the OpenMP library on Mac and gives Homebrew instructions for installing it.evalml.alteryx.com · 2 Oct 2026
Apple M1 caveat: The documentation says not all dependencies support Apple M1 and recommends installing EvalML with core dependencies on that chip.evalml.alteryx.com · 2 Oct 2026
Support and community: The documentation links users to GitHub, Slack, and Stack Overflow.evalml.alteryx.com · 2 Oct 2026
Maker founding year: Alteryx says it was founded in 1997.alteryx.com · 2 Oct 2026

Company

Maker and headquarters: Alteryx lists its headquarters at 3347 Michelson Drive, Suite 400, Irvine, California 92612.alteryx.com · 2 Oct 2026
Founded: 1997evalml.alteryx.com · 28 Sept 2026
Headquarters: Irvine, California, United Statesevalml.alteryx.com · 28 Sept 2026