gbm
Generalized Boosted Regression Models for R
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is gbm?
The gbm package provides an implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. It is used for building predictive models in R.
Key differentiator
“The gbm package is distinguished by its comprehensive support for various loss functions and extensive customization options, making it a powerful tool for advanced predictive modeling tasks in R.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The gbm package lacks comprehensive documentation and practical examples, making it difficult for new users to understand how to effectively use the library.
gbm can be slow when processing large datasets due to its memory usage and computational requirements, which may not scale well without significant hardware resources.
The gbm package does not fully leverage newer R language features such as tidyverse integration, making it less compatible with modern R workflows and libraries.
Fit analysis
Who is it for?
✓ Best for
Researchers and data analysts who need a robust implementation of gradient boosting for predictive analytics.
Academics working on machine learning projects requiring R integration.
✕ Not a fit for
Projects that require real-time predictions due to the computational intensity of gradient boosting algorithms.
Applications needing lightweight models with minimal training time.
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 gbm
Step-by-step setup guide with code examples and common gotchas.