gbm
Generalized Boosted Regression Models for R
- Status
- Not yet determined
- Last checked
- Unverified
- License
- Open source
- Pricing
- Free tier · from $0
What it is
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.
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.
When it fits — and when it doesn’t
A good fit 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.
Look elsewhere if
Projects that require real-time predictions due to the computational intensity of gradient boosting algorithms.
Applications needing lightweight models with minimal training time.
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Limited documentation and examples
The gbm package lacks comprehensive documentation and practical examples, making it difficult for new users to understand how to effectively use the library.
- 02
Performance issues with large datasets
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.
- 03
Limited support for modern R features
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.
What it does well
Supports various loss functions for regression and classification tasks.
Allows customization of boosting parameters such as learning rate and tree complexity.
Offers cross-validation to tune model hyperparameters.
Pricing
- Free tier
- YesOpen source — free to use
- Paid from
- $0
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up gbm
Setup steps, common gotchas and what to check after 30 days.