FREEDOM251

Freedom251 › AutoML Software

EvalML

Free plan APILinuxMacSelf-hostedWindows

Visit EvalML Alternatives

EvalML's own site

What EvalML is

EvalML is a free AutoML library for building, optimizing and evaluating machine-learning pipelines with domain-specific objective functions. It can construct pipelines with preprocessing, feature engineering, feature selection and multiple modeling techniques, and includes tools for examining models. Listed automation features include data-quality checks and cross-validation. Users can select standard objectives such as mean squared error, cross entropy and area under the ROC curve, or define custom objectives. EvalML can be combined with Featuretools and Compose for end-to-end supervised machine-learning solutions. Its time-series functionality uses Prophet to predict future values from past ones; that support is still being developed. Tutorials cover fraud prediction, lead scoring, cost-benefit objectives and text data. Installation is available through PyPI, conda-forge or source, with Python 3.9–3.11 supported on the current installation page. Optional dependencies provide XGBoost, CatBoost and plotting support. It supports Linux, macOS, Windows, API use and self-hosted setups, but dependency configuration can vary by platform: Mac use requires OpenMP for LightGBM, and Apple M1 dependency support is incomplete.

Who it suits

EvalML suits people building supervised machine-learning pipelines in code who want automated pipeline construction and custom objectives. Its tutorials also address fraud prediction, lead scoring, cost-benefit objectives and text data.

What it does well

  • Free AutoML library with no trial requirement.
  • Automates pipeline construction and optimization.
  • Supports standard and custom objectives.
  • Includes model inspection tools.
  • Can work with Featuretools and Compose.

What to know first

  • Time-series support is still being developed.
  • Mac requires OpenMP for LightGBM.
  • Apple M1 dependency support is incomplete.
  • Some Windows dependencies may need conda installation.

Limits

EvalML has no free trial because it is offered as free software. Time-series support is still being actively developed, and Apple M1 support for dependencies is incomplete.

Getting started

  1. Choose PyPI, conda-forge, or source as the installation route.
  2. Use Python 3.9–3.11.
  3. Install EvalML and select optional dependencies such as XGBoost, CatBoost, Plotly, or ipywidgets if needed.
  4. For time-series functionality, install the prophet extra.
  5. On Mac, install OpenMP for LightGBM; on Apple M1, use core dependencies.
  6. On Windows pip installs, install numba first for SHAP and prediction explanations, and python-graphviz for plotting utilities.

Verdict

EvalML provides pipeline automation, configurable objectives and model inspection without a listed price. Platform-specific dependency requirements and developing time-series support are worth considering before installation.

Questions

How much does EvalML cost?

EvalML is free and has a free plan.

Is EvalML open source?

Yes. Alteryx describes EvalML as one of its open-source projects.

Which platforms does EvalML support?

It supports API use, Linux, macOS, Windows, and self-hosted environments.

How can I install EvalML?

Installation is available through PyPI, conda-forge, or source. The current installation page supports Python 3.9–3.11.

Does EvalML support custom objectives?

Yes. It includes domain-specific objective functions and an interface for defining custom objectives.

Where can users get support?

The project directs usage questions to Stack Overflow, bug reports and feature requests to GitHub issues, development discussion to Slack, and other questions to [email protected].

Facts from EvalML's own pages

Add-ons
Documented add-ons include an update checker and time-series support using Facebook’s Prophet library. · 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
Automation
The project README lists automation features including data-quality checks and cross-validation. · github.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
Custom objectives
EvalML includes domain-specific objective functions and an interface for defining custom objectives. · github.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
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
Example use cases
Official tutorials cover fraud prediction, lead scoring, cost-benefit objectives, and text data. · evalml.alteryx.com, 2 Oct 2026
Founded
1997 · evalml.alteryx.com, 28 Sept 2026
Headquarters
Irvine, California, United States · evalml.alteryx.com, 28 Sept 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
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
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
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
Maker and headquarters
Alteryx lists its headquarters at 3347 Michelson Drive, Suite 400, Irvine, California 92612. · alteryx.com, 2 Oct 2026
Maker founding year
Alteryx says it was founded in 1997. · 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
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
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
Pipeline construction
EvalML constructs and optimizes pipelines containing preprocessing, feature engineering, feature selection, and multiple modeling techniques. · github.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
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
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
Support and community
The documentation links users to GitHub, Slack, and Stack Overflow. · evalml.alteryx.com, 2 Oct 2026

Where EvalML stands on Freedom251

Look at these too