simple_bayes
A Naive Bayes machine learning implementation in Elixir with multiple models and storage options.
What is simple_bayes?
Simple Bayes is a Naive Bayes classifier implementation for Elixir, providing a simple yet powerful probabilistic approach to text categorization and classification tasks. It enables developers to build scalable machine learning models for applications like spam detection, content categorization, and automatic medical diagnosis by offering multiple algorithm models and flexible storage options.
Elixir developers building text classification systems, such as spam filters, content moderation tools, or document categorization pipelines, who need a scalable, configurable Naive Bayes implementation.
Developers choose Simple Bayes for its clean, efficient implementation that balances simplicity with practical features like multiple storage backends (memory, file system, Dets), configurable models (multinomial, binarized multinomial, Bernoulli), and text processing enhancements (stop word filtering, TF-IDF, stemming).
Overview
A Naive Bayes machine learning implementation in Elixir.
Use Cases
Best For
- Building spam detection systems in Elixir applications that require probabilistic text classification.
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