What are the main features of scikit-learn-contrib/imbalanced-learn?

The main features of scikit-learn-contrib/imbalanced-learn are: Dataset Class Balancing, Class Imbalance Handling, Class Ratio Scaling, Machine Learning Resampling, Undersampling Heuristics, Python Machine Learning Libraries, Imbalanced Data Sampling, Oversampling Logic.

What are some open-source alternatives to scikit-learn-contrib/imbalanced-learn?

Open-source alternatives to scikit-learn-contrib/imbalanced-learn include: alteryx/featuretools — Featuretools is an automated feature engineering library and data transformation framework written in Python. It… chiphuyen/ml-interviews-book — This project is a collection of comprehensive guides and reference materials designed for technical interviews,… rasbt/python-machine-learning-book-2nd-edition — This project is a machine learning educational resource and implementation guide for Python. It provides a collection… hazelcast/hazelcast — Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to… hudson-and-thames/mlfinlab — mlfinlab is a Python machine learning library for finance designed for building and validating models used in… huggingface/datasets — Datasets is a library designed for the management, processing, and sharing of large-scale data collections for machine…

Imbalanced Learn

imbalanced-learn is a dataset balancing framework and Python machine learning extension designed to resample training data and reduce the impact of class imbalance. It provides a toolkit of algorithms for adjusting class distributions to improve model performance on minority class prediction.

As a scikit-learn resampling library, it extends the ecosystem with specialized tools for balancing datasets through over-sampling and under-sampling techniques. This allows for the correction of skewed class proportions to reduce model bias toward the majority class.

The library implements the scikit-learn estimator API with fit and transform methods and provides pipeline-compatible transformers. Its capabilities cover data preprocessing and machine learning resampling, including randomized oversampling logic and heuristic undersampling mechanisms.

Features

Dataset Class Balancing - Provides tools to adjust class distributions in machine learning data to improve minority class prediction.
Class Imbalance Handling - Provides a programmatic framework to resample training data and reduce the impact of class imbalance.
Class Ratio Scaling - Adjusts the number of samples per class to achieve a target distribution before training a classifier.
Machine Learning Resampling - Applies oversampling or undersampling techniques to create a balanced training set for scikit-learn models.
Undersampling Heuristics - Implements heuristic undersampling mechanisms to reduce majority class samples based on density or distance.
Python Machine Learning Libraries - Extends the scikit-learn ecosystem with advanced sampling methods to mitigate dataset bias.
Imbalanced Data Sampling - Applies sampling techniques to datasets with significant class differences to increase model accuracy.
Oversampling Logic - Generates synthetic minority samples by interpolating between existing data points to expand the minority class boundary.
Machine Learning Model APIs - Implements standard fit and transform methods for compatibility with the scikit-learn estimator ecosystem.
Minority Class Prediction - Improves the ability of a classifier to accurately detect rare events or underrepresented categories in data.
Data Transformation Pipelines - Wraps sampling logic into objects that can be integrated into sequential data processing workflows.
Imbalanced Datasets - Module for under-sampling and over-sampling techniques.
Data Validation and Cleaning - Resampling techniques for imbalanced machine learning datasets.
Feature Engineering Tools - Handling imbalanced datasets.

Star history

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

scikit-learn-contribimbalanced-learn

Name: scikit-learn-contrib/imbalanced-learn
Author: scikit-learn-contrib

View on GitHub

7,104 stars1,348 forksPythonMIT22 viewsimbalanced-learn.org

Imbalanced Learn

Features

Dataset Class Balancing - Provides tools to adjust class distributions in machine learning data to improve minority class prediction.
Class Imbalance Handling - Provides a programmatic framework to resample training data and reduce the impact of class imbalance.
Class Ratio Scaling - Adjusts the number of samples per class to achieve a target distribution before training a classifier.
Machine Learning Resampling - Applies oversampling or undersampling techniques to create a balanced training set for scikit-learn models.
Undersampling Heuristics - Implements heuristic undersampling mechanisms to reduce majority class samples based on density or distance.
Python Machine Learning Libraries - Extends the scikit-learn ecosystem with advanced sampling methods to mitigate dataset bias.
Imbalanced Data Sampling - Applies sampling techniques to datasets with significant class differences to increase model accuracy.
Oversampling Logic - Generates synthetic minority samples by interpolating between existing data points to expand the minority class boundary.
Machine Learning Model APIs - Implements standard fit and transform methods for compatibility with the scikit-learn estimator ecosystem.
Minority Class Prediction - Improves the ability of a classifier to accurately detect rare events or underrepresented categories in data.
Data Transformation Pipelines - Wraps sampling logic into objects that can be integrated into sequential data processing workflows.
Imbalanced Datasets - Module for under-sampling and over-sampling techniques.
Data Validation and Cleaning - Resampling techniques for imbalanced machine learning datasets.
Feature Engineering Tools - Handling imbalanced datasets.

Open-source alternatives to Imbalanced Learn

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Frequently asked questions

What does scikit-learn-contrib/imbalanced-learn do?