clj-boost
Wrapper for XGBoost in Clojure
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is clj-boost?
clj-boost is a Clojure wrapper around the popular machine learning library XGBoost, providing developers with an easy-to-use interface to leverage powerful gradient boosting algorithms.
Key differentiator
“clj-boost stands out as the only Clojure-specific wrapper for XGBoost, offering a streamlined experience for developers looking to leverage gradient boosting within their Clojure projects.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Low number of contributors and infrequent updates on GitHub
Clojure-to-Python interface can introduce latency in model training and prediction
Advanced functionalities require understanding of both Clojure and Python, limiting accessibility
Requires setting up a Clojure environment along with XGBoost's dependencies in Python
Fit analysis
Who is it for?
✓ Best for
Clojure developers looking to integrate gradient boosting algorithms into their applications without leaving the Clojure ecosystem.
Projects that require a seamless integration of machine learning with existing Clojure codebases.
✕ Not a fit for
Developers who prefer working in languages other than Clojure
Teams requiring real-time model training and inference capabilities
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
Works well with
Next step
Get Started with clj-boost
Step-by-step setup guide with code examples and common gotchas.