awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
rasbt avatar

rasbt/mlxtend

0
View on GitHub↗

Mlxtend

mlxtend is a pure Python machine learning extension library that provides additional tools for association rule mining, ensemble learning, and feature selection. It is built on numpy and pandas, with all data operations accepting and returning pandas DataFrames, and custom estimators inherit from scikit-learn’s base classes to offer a uniform fit-predict interface compatible with grid search.

The library implements the Apriori algorithm for mining frequent itemsets from transaction data and generating association rules with confidence and lift metrics. For classification, it combines multiple base models using majority voting, stacking, or bagging to produce a single ensemble prediction. Feature selection is handled through methods such as greedy sequential selection, which iteratively adds or removes features guided by cross-validated performance scores from a wrapped estimator.

All algorithms are implemented without compiled extensions, relying solely on standard Python libraries for numerical and tabular computation. The package is published on the Python Package Index and can be installed via pip.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI
rasbt.github.io/mlxtend
↗

Features

  • Python Machine Learning Libraries - Pure Python library extending scikit-learn with tools for association rules, feature selection, and ensemble learning.
  • Association Rule Learning - Discovers association rules from transaction data, revealing hidden co-occurrence patterns.
  • Ensemble Learning Libraries - Ships an ensemble learning library for combining models via voting, stacking, and bagging.
  • Frequent Itemset Mining - Implements the Apriori algorithm to mine frequent itemsets and generate association rules from transaction data.
  • Majority-Vote Ensembles - Ships a majority-vote classifier that combines predictions from multiple base models.
  • Model Ensembling - Combines models using voting, stacking, and bagging to improve predictive performance and robustness.
  • Automated Feature Selection Tools - Selects relevant features using sequential selection algorithms guided by cross-validated performance scores.
  • Minimal Feature Subset Selectors - Provides greedy sequential forward/backward feature selection with cross-validated scoring.
  • Estimator Interfaces - Custom estimators inherit from scikit-learn base classes for a uniform fit-predict interface and grid-search compatibility.
  • General Machine Learning - Collection of useful tools for data science tasks.
  • Machine Learning - Collection of helper modules for data analysis and machine learning.
  • Machine Learning Algorithms - Collection of helper modules for data analysis and machine learning workflows.
  • Machine Learning Frameworks - Collection of useful machine learning extensions.
  • Machine Learning Packages - Collection of useful tools for daily data science tasks.
  • Model Ensembles - Ensemble voting and stacking regressors.
5,114 stars·898 forks·Python·other·37 views

Star history

Star history chart for rasbt/mlxtendStar history chart for rasbt/mlxtend

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does rasbt/mlxtend do?

mlxtend is a pure Python machine learning extension library that provides additional tools for association rule mining, ensemble learning, and feature selection. It is built on numpy and pandas, with all data operations accepting and returning pandas DataFrames, and custom estimators inherit from scikit-learn’s base classes to offer a uniform fit-predict interface compatible with grid search.

What are the main features of rasbt/mlxtend?

The main features of rasbt/mlxtend are: Python Machine Learning Libraries, Association Rule Learning, Ensemble Learning Libraries, Frequent Itemset Mining, Majority-Vote Ensembles, Model Ensembling, Automated Feature Selection Tools, Minimal Feature Subset Selectors.

Which projects share features with rasbt/mlxtend?

Projects with overlapping indexed features include: davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and… eriklindernoren/ml-from-scratch — This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built… haifengl/smile — Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of… dswah/pygam — [CONTRIBUTORS WELCOME] Generalized Additive Models in Python. christophm/rulefit — Python implementation of the rulefit algorithm. aksnzhy/xlearn — High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization…

Projects sharing features with Mlxtend

These projects share indexed features with Mlxtend. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • davisking/dlibdavisking avatar

    davisking/dlib

    14,472View on GitHub↗

    dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and utilities for building predictive modeling applications and performing statistical analysis on large datasets within native C++ environments. The project functions as a binding library that wraps low-level C++ machine learning algorithms into high-level Python scripting interfaces. This allows for the integration of high-performance native implementations with Python for machine learning development. The framework covers the implementation of predictive models, the execution of mach

    C++c-plus-pluscomputer-visiondeep-learning
    View on GitHub↗14,472
  • eriklindernoren/ml-from-scratcheriklindernoren avatar

    eriklindernoren/ML-From-Scratch

    33,283View on GitHub↗

    This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built from scratch. By avoiding high-level library abstractions, it serves as a pedagogical reference for understanding the mathematical foundations and core mechanics of supervised learning, unsupervised learning, and reinforcement learning models. The repository distinguishes itself through a modular approach to model construction, allowing users to build custom neural networks by chaining independent functional blocks. It covers a wide range of techniques, including gradient-base

    Pythondata-miningdata-sciencedeep-learning
    View on GitHub↗33,283
  • haifengl/smilehaifengl avatar

    haifengl/smile

    6,387View on GitHub↗

    Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of algorithms for classification, regression, and clustering, implemented natively for Java, Scala, and Kotlin. The project also functions as a deep learning framework, a natural language processing library, and an inference engine for large language models. The library distinguishes itself through GPU acceleration via LibTorch bindings and support for the ONNX model interchange format. It includes specialized capabilities for large language model inference, featuring Byte-Pair Encodin

    Java
    View on GitHub↗6,387
  • aksnzhy/xlearnaksnzhy avatar

    aksnzhy/xlearn

    3,095View on GitHub↗

    High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

    C++
    View on GitHub↗3,095
Compare all 30 related projects→