autogluon.features
Fast and Accurate ML in 3 Lines of Code
Decision gist · record as of 2026-08-14
Yes, if you are building on AutoGluon or need automated feature engineering for tabular, time series, or multimodal data. The package is actively maintained, has no known vulnerabilities, and installs with low friction. It is most valuable as part of the full AutoGluon ecosystem; standalone utility depends on your pipeline architecture.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10–3.13; not compatible with older or newer Python versions.
- Low friction install with a pure Python wheel.
- Actively maintained as of 8 days ago with a large repository (10596 stars) and recent activity.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for both open and proprietary projects.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 10,596 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 540,326 downloads/mo, #6,106 on PyPI
Alternatives
Verify before relying
pip install autogluon.features
from autogluon.features import FeatureGenerator
from autogluon.common import space
# Prepare features for tabular data
generator = FeatureGenerator()
features = generator.fit_transform(data)- Specific feature engineering algorithms and transformations supported beyond the runtime dependencies.
- Whether this package is typically used standalone or primarily as part of the full AutoGluon suite.
- Performance characteristics and scalability limits for large datasets.
What it is and what it does
autogluon.features is the feature engineering component of AutoGluon, AWS's automated machine learning framework. It handles the preprocessing and feature extraction steps that typically require manual tuning in ML pipelines, working with tabular, time series, and multimodal data. The package sits on top of numpy, pandas, scikit-learn, and autogluon.common, providing automated transformations and selection to prepare raw data for model training.
The package is designed to reduce boilerplate in the data preparation phase, fitting into AutoGluon's broader "3 lines of code" philosophy for end-to-end ML. It supports Python 3.10–3.13 across Linux, macOS, and Windows, and is actively maintained with recent commits and a large community (10596 repository stars). No known security vulnerabilities are recorded.
Use it for
- Automatically engineer features from raw tabular datasets before training predictive models.
- Preprocess time series data for forecasting tasks within an AutoGluon pipeline.
- Extract and transform features from multimodal data (text, images, structured fields) for unified ML workflows.
- Reduce manual feature engineering effort in rapid prototyping and proof-of-concept projects.
- Integrate feature preparation into end-to-end AutoML workflows that also handle model selection and hyperparameter tuning.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building on AutoGluon or need automated feature engineering for tabular, time series, or multimodal data.
The package is actively maintained, has no known vulnerabilities, and installs with low friction. It is most valuable as part of the full AutoGluon ecosystem; standalone utility depends on your pipeline architecture.
Install
autogluon-features on PyPI
Before you install
Low friction install with a pure Python wheel. Actively maintained as of 8 days ago with a large repository (10596 stars) and recent activity. Depends on numpy, pandas, scikit-learn, and autogluon.common—all standard ML stack components.
Requires Python 3.10–3.13; not compatible with older or newer Python versions.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for both open and proprietary projects.
Quickstart
pip install autogluon.features
from autogluon.features import FeatureGenerator
from autogluon.common import space
# Prepare features for tabular data
generator = FeatureGenerator()
features = generator.fit_transform(data)Verify before relying
- Specific feature engineering algorithms and transformations supported beyond the runtime dependencies.
- Whether this package is typically used standalone or primarily as part of the full AutoGluon suite.
- Performance characteristics and scalability limits for large datasets.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpypandasscikit-learnautogluon.common |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 540,326 / month, #6,106 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Customer ServiceIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Financial and Insurance IndustryIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchIntended Audience :: Telecommunications IndustryOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Scientific/Engineering :: Information AnalysisTopic :: Software Development |
Evidence: autogluon_features-1.6.1-py3-none-any.whl
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See also autogluon · autogluon.tabular · autogluon.common · autogluon.core · autogluon.timeseries · autogluon.vision · autogluon.multimodal · autogluon.text · tabpfn · cleanlab