EvalML

6.4easy start · #12 of 28
in AutoML Software

Free to practise onnot on record
Free trialnot on record
Well documentedyes
Runs where you workyes

Runs on api, Linux, Mac, self-hosted, Windows.

Visit site Alternatives

EvalML is ranked #12 of 28 in AutoML software on The Geeks Club. It runs on API, Linux, macOS, Self-hosted, Windows.

Compared on AutoML software

Free plan: Yes
Feature engineering: Yes
Automated model selection: Yes
Model explainability: Yes
Workflow interface: code
Hosting model: self_hosted

Best EvalML alternatives

See all 20

BigML 0 of 4 in the starter kit Lesson 17.6 JADBio 0 of 4 in the starter kit Lesson 27.6 Amazon SageMaker Autopilot 0 of 4 in the starter kit Lesson 37.5 AutoKeras 0 of 4 in the starter kit Lesson 47.0 FEDOT 0 of 4 in the starter kit Lesson 57.0 LightAutoML 0 of 4 in the starter kit Lesson 66.9

The record

What it does: EvalML is an AutoML library that builds, optimizes, and evaluates machine-learning pipelines using domain-specific objective functions.
End-to-end solutions: EvalML can be combined with Featuretools and Compose to create end-to-end supervised machine-learning solutions.
Automation: The project README lists automation features including data-quality checks and cross-validation.
Pipeline construction: EvalML constructs and optimizes pipelines containing preprocessing, feature engineering, feature selection, and multiple modeling techniques.
Model understanding: EvalML provides tools to understand and introspect models.
Custom objectives: EvalML includes domain-specific objective functions and an interface for defining custom objectives.
Installation: EvalML can be installed from PyPI, conda-forge, or source, with Python 3.9–3.11 supported on the current installation page.
Optional dependencies: XGBoost and CatBoost support modeling pipelines, while Plotly and ipywidgets support plotting in AutoML searches; these dependencies are optional.
Time-series add-on: Time-series support uses Facebook’s Prophet library, installed with the prophet extra.
Platform limitations: On Windows, numba and Graphviz may need conda installation and XGBoost may not be pip-installable in some environments.
Mac limitations: Running EvalML on Mac requires the OpenMP library for LightGBM, and M1 Macs have incomplete dependency support with core-dependencies installation recommended.
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.
Open-source status: Alteryx describes EvalML as one of its open-source projects and links to its documentation and GitHub project files.
Intended users: Alteryx says EvalML can guide people who want to understand how a system works or generate accurate predictions to an efficient solution.

Where it ranks on The Geeks Club

Best AutoML Software in 2026#12 of 28

Is EvalML yours?

Claim it for free: prove the domain, then correct facts, plans and screenshots. An editor reviews every change.

Claim it free Promote EvalML

Sources

evalml.alteryx.com/en/stable/· checked 2 Oct 2026
github.com/alteryx/evalml/blob/main/README.md· checked 2 Oct 2026
evalml.alteryx.com/en/stable/install.html· checked 2 Oct 2026
alteryx.com/open-source· checked 2 Oct 2026
evalml.alteryx.com/en/stable/user_guide/objectives.html· checked 2 Oct 2026
evalml.alteryx.com/en/stable/user_guide/timeseries.html· checked 2 Oct 2026
evalml.alteryx.com/en/stable/tutorials.html· checked 2 Oct 2026
alteryx.com/contact-us· checked 2 Oct 2026
alteryx.com/about-us/leadership· checked 2 Oct 2026
evalml.alteryx.com· checked 28 Sept 2026