{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Implements the mRMR (minimum Redundancy - Maximum Relevance) feature selection algorithm to identify the smallest subset of relevant features for classification and regression tasks across Pandas, Polars, Spark, and BigQuery.","skillfed_tags":["feature-selection","machine-learning","dimensionality-reduction"],"use_cases":["Reduce dataset dimensionality in production ML pipelines where frequent, automated feature selection is needed without manual tuning.","Identify the most predictive features in high-dimensional datasets to lower memory and computation costs in model training and inference.","Improve model interpretability by selecting a minimal set of features that explain predictions while maintaining accuracy.","Perform feature selection on large-scale data stored in Spark or BigQuery without loading entire datasets into memory.","Benchmark feature importance across classification and regression tasks to guide domain expert review of model inputs."],"what_it_does":"mrmr_selection implements a minimal-optimal feature selection algorithm designed to find the smallest subset of features that retain predictive power for a machine learning task. Unlike all-relevant methods that identify every feature with some relationship to the target, mRMR prioritizes efficiency by selecting only the most informative features while minimizing redundancy among them.\n\nThe package provides separate modules for Pandas, Polars, Spark, and BigQuery, each exposing mrmr_classif (for categorical targets) and mrmr_regression (for numeric targets) functions. It depends on pandas, numpy, scipy, joblib, category-encoders, jinja2, tqdm, and polars. The algorithm returns a ranked list of the top K selected features, allowing further filtering if needed.","worth_installing":"Yes, if you need minimal-optimal feature selection and accept dormant maintenance. The algorithm is well-established and used in production systems, dependencies are stable, and no known vulnerabilities exist. Install friction is low. However, the last release was 2023-06-30 with no recent commits, so expect no active support for new dependency versions or bug fixes. GPL v3.0 licensing is a hard constraint for proprietary projects. Best suited for open-source or internal ML workflows where feature selection is a one-time or infrequent task."},"id":"mrmr-selection","links":{"html":"https://skillfed.io/packages/mrmr-selection","md":"https://skillfed.io/packages/mrmr-selection.md","pypi":"https://pypi.org/project/mrmr-selection/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-06-30","license_spdx":null,"license_treatment":"unclear","name":"mrmr-selection","python_support":"unspecified","summary":"minimum-Redundancy-Maximum-Relevance algorithm for feature selection"},"popularity":{"monthly_downloads":73500,"position":14988,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.8"}