{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"Feature-engine provides transformers for engineering, selecting, and preprocessing features in machine learning pipelines, following scikit-learn's fit/transform interface.","skillfed_tags":["feature-engineering","scikit-learn-compatible","data-preprocessing"],"use_cases":["Encode rare categorical values into a single 'Rare' category to reduce cardinality before modeling.","Impute missing values using mean, median, or arbitrary strategies learned from training data.","Remove or cap outliers using statistical methods like Winsorization before training.","Create datetime-derived features like day-of-week or cyclical encodings from timestamp columns.","Select the most predictive features using correlation, information value, or model-based elimination.","Transform skewed variables using log, Box-Cox, or Yeo-Johnson transformations for normality."],"what_it_does":"Feature-engine is a Python library that wraps common feature engineering and selection tasks into scikit-learn-compatible transformers. It provides methods for handling missing data, encoding categorical variables, discretising continuous features, capping or removing outliers, transforming and scaling variables, creating new features from existing ones, and selecting the most informative features for modeling. The library depends on numpy, pandas, scikit-learn, scipy, and statsmodels to perform its transformations.\n\nYou use it by instantiating a transformer (e.g., RareLabelEncoder, MeanImputer, DropCorrelatedFeatures), calling fit() on training data to learn parameters, and then calling transform() on new data to apply the same transformation. This design integrates naturally into scikit-learn pipelines and cross-validation workflows, making it straightforward to build reproducible feature engineering workflows without writing custom code for each task.","worth_installing":"Yes. Feature-engine is actively maintained, has no known vulnerabilities, installs with low friction, and provides a comprehensive suite of production-ready transformers that integrate seamlessly with scikit-learn workflows. It is well-suited for anyone building machine learning pipelines who wants to avoid writing repetitive feature engineering code."},"id":"feature-engine","links":{"html":"https://skillfed.io/packages/feature-engine","md":"https://skillfed.io/packages/feature-engine.md","pypi":"https://pypi.org/project/feature-engine/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-27","license_spdx":null,"license_treatment":"permissive","name":"feature-engine","python_support":"supports_current","summary":"Feature engineering and selection package with Scikit-learn's fit transform functionality"},"popularity":{"monthly_downloads":331267,"position":7518,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.9.4"}