awesome-repositories.com
Blog
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetÀ proposNotre méthodologiePresseServeur MCP
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 dépôts

Awesome GitHub RepositoriesFeature Engineering Tools

Utilities for modifying dataset attributes and improving data quality for analysis.

Distinct from Data Collections & Datasets: Shortlist candidates focus on provenance (attribution) or generic collections, not the manipulation of numerical precision and features

Explore 2 awesome GitHub repositories matching data & databases · Feature Engineering Tools. Refine with filters or upvote what's useful.

Awesome Feature Engineering Tools GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • sjwhitworth/golearnAvatar de sjwhitworth

    sjwhitworth/golearn

    9,438Voir sur GitHub↗

    GoLearn is a machine learning library for the Go programming language. It provides a supervised learning framework and a toolkit for building, training, and evaluating predictive models through a standardized interface. The project implements a data frame system that loads CSV files into structured grids for matrix operations. It includes a preprocessing library for discretizing continuous variables and a model evaluation toolkit that utilizes confusion matrices and cross-validation to measure precision and recall. The library covers data engineering and management, including the ability to

    Allows sorting data, adding new features, and setting numerical precision for floating-point values to improve data quality.

    Go
    Voir sur GitHub↗9,438
  • blue-yonder/tsfreshAvatar de blue-yonder

    blue-yonder/tsfresh

    9,249Voir sur GitHub↗

    tsfresh is an automated feature engineering tool and library designed to extract statistical characteristics from raw time series data. It transforms sequential data into tabular datasets, converting time series into a flat format where each row represents a unique entity and columns represent extracted features. The project distinguishes itself through a parallel data processing framework that distributes heavy computational workloads across multiple CPU cores. It also implements hypothesis-based feature selection to identify the most predictive characteristics and filter out irrelevant ones

    Provides an automated system for transforming raw sequence data into predictive features through statistical calculations.

    Jupyter Notebookdata-sciencefeature-extractiontime-series
    Voir sur GitHub↗9,249
  1. Home
  2. Data & Databases
  3. Feature Engineering Tools