Simple Bayes
A Simple Naive Bayes implementation in Elixir for text classification tasks.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Simple Bayes?
Simple Bayes is an efficient and straightforward Naive Bayes classifier implemented in the Elixir programming language. It's ideal for developers looking to integrate a lightweight, yet effective machine learning model into their applications without the complexity of larger frameworks.
Key differentiator
“Simple Bayes stands out as an easy-to-integrate Naive Bayes classifier specifically tailored for Elixir applications, offering simplicity and efficiency without the overhead of larger machine learning frameworks.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Simple Bayes is implemented in Elixir, which limits its usage to projects that can integrate with or are written in Elixir.
As an open-source project with a focus on a niche language like Elixir, Simple Bayes may have less active contributors and fewer resources for troubleshooting and learning.
Simple Bayes is lightweight but might struggle with very large datasets or complex classification tasks compared to more robust machine learning frameworks.
Fit analysis
Who is it for?
✓ Best for
Elixir developers who need a lightweight text classification tool for their applications
Projects requiring efficient and simple Naive Bayes implementation without external dependencies
✕ Not a fit for
Developers looking for comprehensive machine learning libraries with multiple algorithms
Applications that require real-time, high-throughput text processing beyond the capabilities of a single-threaded Elixir process
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Next step
Get Started with Simple Bayes
Step-by-step setup guide with code examples and common gotchas.