Difference between revisions of "Boosted Random Forest"
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* [[Random Forest (or) Random Decision Forest]] | * [[Random Forest (or) Random Decision Forest]] | ||
* [[XGBoost; eXtreme Gradient Boosted trees]] | * [[XGBoost; eXtreme Gradient Boosted trees]] | ||
| + | * [[Gradient Boosting Machine (GBM)]] | ||
Random forest (ensemble method) builds multiple decision trees and merges them together to get a more accurate and stable prediction. Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it’s simplicity and the fact that it can be used for both classification and regression tasks. [http://towardsdatascience.com/the-random-forest-algorithm-d457d499ffcd The Random Forest Algorithm | Niklas Donges @ Towards Data Science] | Random forest (ensemble method) builds multiple decision trees and merges them together to get a more accurate and stable prediction. Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it’s simplicity and the fact that it can be used for both classification and regression tasks. [http://towardsdatascience.com/the-random-forest-algorithm-d457d499ffcd The Random Forest Algorithm | Niklas Donges @ Towards Data Science] | ||
<youtube>QHOazyP-YlM</youtube> | <youtube>QHOazyP-YlM</youtube> | ||
Revision as of 14:23, 27 July 2020
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- AI Solver
- Capabilities
- Boosting
- Random Forest (or) Random Decision Forest
- XGBoost; eXtreme Gradient Boosted trees
- Gradient Boosting Machine (GBM)
Random forest (ensemble method) builds multiple decision trees and merges them together to get a more accurate and stable prediction. Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it’s simplicity and the fact that it can be used for both classification and regression tasks. The Random Forest Algorithm | Niklas Donges @ Towards Data Science
