Difference between revisions of "Boosting"
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* [[Gradient Boosting Machine (GBM)]] | * [[Gradient Boosting Machine (GBM)]] | ||
| + | * Gradient [[(Boosted) Decision Tree]] (GBDT) | ||
| + | * [http://en.wikipedia.org/wiki/AdaBoost Adaptive Boosting (AdaBoost)] | ||
* [[Gradient Descent Optimization & Challenges]] | * [[Gradient Descent Optimization & Challenges]] | ||
* [[Objective vs. Cost vs. Loss vs. Error Function]] | * [[Objective vs. Cost vs. Loss vs. Error Function]] | ||
Revision as of 07:32, 4 February 2019
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- Gradient Boosting Machine (GBM)
- Gradient (Boosted) Decision Tree (GBDT)
- Adaptive Boosting (AdaBoost)
- Gradient Descent Optimization & Challenges
- Objective vs. Cost vs. Loss vs. Error Function
- Boosting | Wikipedia
- A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning | Jason Brownlee
Gradient Boosting Algorithm uses multiple weak algorithms to create a more powerful accurate algorithm. Instead of using a single estimator, having multiple will create a more stable and robust algorithm. The specialty of Gradient Boosting Algorithms is their higher accuracy. There are several Gradient Boosting Algorithms. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium
- XGBoost — uses liner and tree algorithms
- LightGBM - A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft. LightGBM
