msamsami/wnb
General (mixed) and weighted naive Bayes classifiers.
This tool helps data scientists and machine learning engineers build more accurate classification models. It takes your raw dataset with various types of features (numbers, categories, etc.) and provides a trained classifier that can predict outcomes more effectively than standard methods, especially for complex or imbalanced datasets. You get a model that can make better predictions, improving tasks like fraud detection or medical diagnosis.
No commits in the last 6 months. Available on PyPI.
Use this if you need a classification model that can handle diverse data types and improve prediction accuracy by weighting features or managing class imbalance, while maintaining a familiar scikit-learn interface.
Not ideal if your dataset only contains numerical data that fits a simple Gaussian distribution and you don't require advanced weighting or handling of class imbalance.
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23
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Language
Python
License
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Category
Last pushed
Sep 23, 2025
Commits (30d)
0
Dependencies
4
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