gbrt | 梯度迭代回归树 | Machine Learning library

by lokicui C++ Version: Current License: No License

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kandi X-RAY | gbrt Summary

gbrt is a C++ library typically used in Artificial Intelligence, Machine Learning applications. gbrt has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

梯度迭代回归树(Gradient boosting regression tree).

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Quality

Security

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Support

gbrt has a low active ecosystem.

It has 3 star(s) with 5 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

gbrt has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of gbrt is current.

Quality

gbrt has 0 bugs and 0 code smells.

Security

gbrt has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

gbrt code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gbrt does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

gbrt releases are not available. You will need to build from source code and install.

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gbrt Key Features

No Key Features are available at this moment for gbrt.

gbrt Examples and Code Snippets

No Code Snippets are available at this moment for gbrt.

Community Discussions

Trending Discussions on gbrt

Numerical jump in sklearn GradientBoostingRegressor

QUESTION

Numerical jump in sklearn GradientBoostingRegressor

Asked 2020-May-11 at 09:19

I have been investigating a "hand-rolled" version of a gradient boosted regression tree. I find that the errors agree very well with the sklearn GradientBoostingRegressor module until I increase the tree building loop above a certain value. I am not sure if this is a bug in my code or a feature of the algorithm that is manifesting itself, so I was looking for some guidance as to what may be happening. My full code listing that uses the Boston housing market data is shown below, and below that the output when I change the loop parameter.

...

ANSWER

Answered 2020-May-11 at 09:19

I believe there are two sources of differences here. The biggest one is the randomness in the DecisionTreeRegressor.fit method. While you set your random seeds to 42 in both the GradientBoostingRegressor and in all of the DecisionTreeRegressors, your DecisionTreeRegressor training loop does not duplicate the way GradientBoostingRegressor handles the random seed. In your loop, you set the seed on each iteration. In the GradientBoostingRegressor.fit method, the seed is (I assume) set only once at the beginning of training. I've modified your code as follows:

Source https://stackoverflow.com/questions/61708639

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install gbrt

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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