{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/7"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"}],"enrichment":{"capability":"Automates feature engineering and preprocessing for machine learning pipelines, integrating with AutoGluon's broader ML automation framework to handle tabular, time series, and multimodal data preparation.","skillfed_tags":["automl","feature-engineering","data-preprocessing"],"use_cases":["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."],"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.\n\nThe 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\u20133.13 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.","worth_installing":"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."},"id":"autogluon-features","links":{"html":"https://skillfed.io/packages/autogluon-features","md":"https://skillfed.io/packages/autogluon-features.md","pypi":"https://pypi.org/project/autogluon-features/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"autogluon.features","python_support":"supports_current","summary":"Fast and Accurate ML in 3 Lines of Code"},"popularity":{"monthly_downloads":540326,"position":6106,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.6.1"}