Gradient Boosting Machine
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 October 2001
- Authors
- Jerome H. Friedman
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Mathematics
- Task
- Pattern classification, Binary classification, Regression
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 5,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Unknown
- Citations
- 17,891
Sources
Where this record came from and when it was last checked.
- Reference
- Greedy function approximation: A gradient boosting machine
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Gradient Boosting Machine was published by Stanford University, in United States of America, in October 2001. academia is the category the publisher falls under.
It works in Mathematics, and is recorded as doing pattern classification, Binary classification, Regression.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
It was trained on about 5,000 tokens of text.
The reason it appears in this catalogue at all is highly cited.
Answers
Gradient Boosting Machine — common questions
How many parameters does Gradient Boosting Machine have?
No parameter count has been published for Gradient Boosting Machine, which is why no memory or speed figure appears on this page.
Who created Gradient Boosting Machine?
Gradient Boosting Machine was published by Stanford University, based in United States of America, categorised as academia.
When was Gradient Boosting Machine released?
Gradient Boosting Machine was published in October 2001. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Gradient Boosting Machine used for?
Gradient Boosting Machine works in Mathematics, and is recorded as handling pattern classification, Binary classification, Regression. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run Gradient Boosting Machine?
None. Gradient Boosting Machine is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Gradient Boosting Machine open source?
The licensing for Gradient Boosting Machine was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.