Difference between revisions of "Random Forest (or) Random Decision Forest"
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| − | Random forest 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] |
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== Two-Class Decision Forest == | == Two-Class Decision Forest == | ||
Revision as of 21:27, 3 June 2018
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
Two-Class Decision Forest
