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evalml

An AutoML library written in Python

GraphCanon updated Sep 30, 2026 · GitHub synced Sep 30, 2026

44views this month

850 stars96 forksLast push Jan 14, 2026 Python BSD-3-Clause

Decision brief

EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license.

Good fit when

You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical.
Your project prioritizes accessibility through multiple installation options, including PyPI and conda-forge.

Avoid when

You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box.
Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities.

Pricing:: freemium - Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.
Requirements:: Min 2 GB RAM

Observed Jul 17, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report

Maintenance: Slowing (258d since push); As of Sep 30, 2026
Provenance: Not a fork · Organization account; As of Sep 30, 2026
Security (OSV): No lockfile; As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install evalml

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Same-category neighbours. No typed graph edges are catalogued for this tool yet.

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

EvalML provides automated machine learning capabilities including feature engineering, selection, and hyperparameter tuning for Python users.

Capability facts

Languages: python
Source: github.language+pyproject.toml · Sep 30, 2026

Categories

Evaluation & Observability Model Training

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 30, 2026)

pip install evalml

Tags

automl data-science feature-engineering feature-selection hyperparameter-tuning machine-learning model-selection optimization

README

Installation Install from PyPI: or from the conda forge channel on conda:

For agents

This page has a .md twin and JSON over the API.

/tools/alteryx-evalml.md GET /api/graphcanon/tools/alteryx-evalml GET /api/graphcanon/graph?tool=alteryx-evalml

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