Difference between revisions of "LightGBM"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
}} | }} | ||
| − | [ | + | [https://www.youtube.com/results?search_query=LightGBM+gradient+boosting+Boosted+Tree YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=LightGBM+gradient+boosting+Boosted+Tree ...Google search] |
* [[AI Solver]] | * [[AI Solver]] | ||
** [[...predict values]] | ** [[...predict values]] | ||
| + | * [[Case Studies]] | ||
| + | ** [[Astronomy]] | ||
| + | ** [[Screening; Passenger, Luggage, & Cargo]] | ||
* [[Capabilities]] | * [[Capabilities]] | ||
* [[Gradient Boosting Machine (GBM)]] | * [[Gradient Boosting Machine (GBM)]] | ||
| + | * [[XGBoost; eXtreme Gradient Boosted trees]] | ||
* [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]] | * [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]] | ||
* [[(Boosted) Decision Tree]] | * [[(Boosted) Decision Tree]] | ||
* [[Boosted Random Forest]] | * [[Boosted Random Forest]] | ||
| − | * [ | + | * [https://en.wikipedia.org/wiki/Boosting_(machine_learning) Boosting | Wikipedia] |
| − | * [ | + | * [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/boosted-decision-tree-regression Boosted Decision Tree Regression | Microsoft] |
| + | * [https://github.com/microsoft/LightGBM LightGBM, Light Gradient Boosting Machine] - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. LightGBM is under the umbrella of the [https://github.com/microsoft/dmtk DMTK project of Microsoft | GitHub] | ||
| − | Microsoft's gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient by using histogram-based algorithms, which bucket continuous feature (attribute) values into discrete bins. This speeds up training and reduces memory usage. | + | Microsoft's gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient by using histogram-based algorithms, which bucket continuous feature (attribute) values into discrete bins. This speeds up training and reduces [[memory]] usage. |
<youtube>OQHlmscvkRI</youtube> | <youtube>OQHlmscvkRI</youtube> | ||
<youtube>V5158Oug4W8</youtube> | <youtube>V5158Oug4W8</youtube> | ||
Latest revision as of 21:46, 2 March 2024
YouTube search... ...Google search
- AI Solver
- Case Studies
- Capabilities
- Gradient Boosting Machine (GBM)
- XGBoost; eXtreme Gradient Boosted trees
- Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking
- (Boosted) Decision Tree
- Boosted Random Forest
- Boosting | Wikipedia
- Boosted Decision Tree Regression | Microsoft
- LightGBM, Light Gradient Boosting Machine - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. LightGBM is under the umbrella of the DMTK project of Microsoft | GitHub
Microsoft's gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient by using histogram-based algorithms, which bucket continuous feature (attribute) values into discrete bins. This speeds up training and reduces memory usage.
