{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"Performs Multiple Imputation by Chained Equations (MICE) using LightGBM to fill missing values in pandas DataFrames and numpy arrays with statistical rigor and speed.","skillfed_tags":["missing-data","statistical-imputation","machine-learning-preprocessing"],"use_cases":["Fill missing values in survey or medical datasets while preserving statistical properties for downstream analysis.","Generate multiple imputed datasets to assess how missing-data uncertainty affects model predictions or statistical inference.","Impute new, unseen data using models trained on a reference dataset without retraining.","Preprocess data with missing values as part of a scikit-learn machine learning pipeline.","Handle datasets with mixed numeric and categorical columns automatically without manual encoding."],"what_it_does":"miceforest implements Multiple Imputation by Chained Equations (MICE), a statistical method for handling missing data by creating multiple plausible imputed datasets. It uses LightGBM as the underlying predictive model, which provides speed and memory efficiency compared to traditional MICE implementations. The package handles both numeric and categorical data automatically and supports mean matching to preserve the distribution of imputed values.\n\nThe package is designed for both research and production use. You can create a single imputed dataset for quick analysis, or generate multiple imputed datasets to quantify uncertainty from missing values. It integrates with pandas and numpy, fits into scikit-learn pipelines, and allows you to train models on complete data and apply them to new datasets with missing values. Data can be imputed in place to reduce memory overhead, and trained kernels can be saved and reloaded for consistent imputation of new data.","worth_installing":"Yes, if you need production-grade MICE imputation with speed and flexibility. The low install friction, MIT license, and lack of known vulnerabilities make it a safe choice. The aging maintenance status (291 days since last release) is a minor concern but not a blocker\u2014the package is stable and the repository remains active. Install if your workflow requires multiple imputation or if you're working with missing data in pandas/numpy and want LightGBM's speed over traditional methods."},"id":"miceforest","links":{"html":"https://skillfed.io/packages/miceforest","md":"https://skillfed.io/packages/miceforest.md","pypi":"https://pypi.org/project/miceforest/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-10-27","license_spdx":"MIT","license_treatment":"permissive","name":"miceforest","python_support":"supports_current","summary":"Multiple Imputation by Chained Equations with LightGBM"},"popularity":{"monthly_downloads":90756,"position":13564,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"6.0.5"}