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@mevlt01001 mevlt01001 released this 13 Jul 21:03
· 28 commits to main since this release
v0.3.0
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v0.3.0

Overview

Feature Selection Toolkit v0.3.0 adds a number of improvements and new functions, providing a more powerful and flexible toolkit for feature selection in machine learning projects. This release comes with new methods, improved documentation and bug fixes.

New Features

  1. Support Vector Machines (SVC, SVR): Support Vector Machines have been added for both classification (SVC) and regression (SVR). This extends the range of models that can be used for feature selection.
  2. XGBoost Models: Integrated XGBClassifier and XGBRegressor for powerful gradient boosting based feature selection.

Improvements

  1. Improved Documentation: Detailed documentation and use cases have been added for all methods, making it easier for users to understand and apply feature selection techniques.
  2. Alert Management: Added alert suppression to improve user experience during operation.
  3. API Consistency: Ensured consistency for parameter naming and method signatures across all methods, making API usage more intuitive.
  4. Code Improvements: Performance improvements and code edits have been made, resulting in a faster and more efficient working environment.

Bug Fixes

  • Warnings: Fixed some warnings in the previous version.
  • Model Compatibility: Fixed issues with some models not working with certain data types.

Installation and Use

pip install feature-selection-toolkit

GitHub

You can visit our GitHub page for more information and source code:
Feature Selection Toolkit GitHub

Feedback

We welcome your feedback about our library! You can share your contributions and suggestions with us to build a better and more efficient toolkit.

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