Boosted Random Forest - Revision history

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2023-03-28T08:21:38Z

Text replacement - "http:" to "https:"

BPeat at 19:33, 27 July 2020

2020-07-27T19:33:00Z

BPeat at 19:23, 27 July 2020

2020-07-27T19:23:55Z

BPeat at 19:22, 27 July 2020

2020-07-27T19:22:08Z

BPeat at 19:19, 27 July 2020

2020-07-27T19:19:02Z

BPeat at 19:17, 27 July 2020

2020-07-27T19:17:14Z

BPeat: Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

2020-07-27T19:15:14Z

Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

New page

{{#seo:
|title=PRIMO.ai
|titlemode=append
|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
}}
[http://www.youtube.com/results?search_query=Random+Forest+Decision+artificial+intelligence YouTube search...]
[http://www.google.com/search?q=Random+Forest+Decision+deep+machine+learning+ML+artificial+intelligence ...Google search]

* [[AI Solver]]
** [[...predict categories]]
* [[Capabilities]]
* [[Boosting]]
* [[Random Forest]]

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]

http://cdn-images-1.medium.com/max/800/1*rLYBTFztVFOpAy7m3OtElg.jpeg

Random forest can be identified as a collection of decision trees as its name says. Each tree tries to estimate a classification and this is called as a “vote”. Ideally, we consider each vote from every tree and chose the most voted classification. [http://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium]

<youtube>QHOazyP-YlM</youtube>

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 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Boosted+Random+Forest+artificial+intelligence+machine+learning YouTube search...]
+[https://www.youtube.com/results?search_query=Boosted+Random+Forest+artificial+intelligence+machine+learning YouTube search...]
-[http://www.google.com/search?q=Boosted+Random+Forest+artificial+intelligence+machine+learning ...Google search]
+[https://www.google.com/search?q=Boosted+Random+Forest+artificial+intelligence+machine+learning ...Google search]
 * [[AI Solver]]
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 * [[Gradient Boosting Machine (GBM)]]
-The ability of generalization by random forests is higher than that by other multi-class classifiers because of the effect of bagging and feature selection. Since random forests based on ensemble learning requires a lot of decision trees to obtain high performance, it is not suitable for implementing the algorithm on the small-scale hardware such as embedded system. In this paper, we propose a boosted random forests in which boosting algorithm is introduced into random forests. Experimental results show that the proposed method, which consists of fewer decision trees, has higher generalization ability comparing to the conventional method. [http://ieeexplore.ieee.org/document/7294983 Boosted random forest | Y. Mishina, M. Tsuchiya, and H. Fujiyoshi - IEEE Xplore]
+The ability of generalization by random forests is higher than that by other multi-class classifiers because of the effect of bagging and feature selection. Since random forests based on ensemble learning requires a lot of decision trees to obtain high performance, it is not suitable for implementing the algorithm on the small-scale hardware such as embedded system. In this paper, we propose a boosted random forests in which boosting algorithm is introduced into random forests. Experimental results show that the proposed method, which consists of fewer decision trees, has higher generalization ability comparing to the conventional method. [https://ieeexplore.ieee.org/document/7294983 Boosted random forest | Y. Mishina, M. Tsuchiya, and H. Fujiyoshi - IEEE Xplore]
 <youtube>9wn1f-30_ZY</youtube>

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 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Random+Forest+Decision+artificial+intelligence YouTube search...]
+[http://www.youtube.com/results?search_query=Boosted+Random+Forest+artificial+intelligence+machine+learning YouTube search...]
-[http://www.google.com/search?q=Random+Forest+Decision+deep+machine+learning+ML+artificial+intelligence ...Google search]
+[http://www.google.com/search?q=Boosted+Random+Forest+artificial+intelligence+machine+learning ...Google search]
 * [[AI Solver]]
@@ Line 16: / Line 16: @@
 * [[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]
+The ability of generalization by random forests is higher than that by other multi-class classifiers because of the effect of bagging and feature selection. Since random forests based on ensemble learning requires a lot of decision trees to obtain high performance, it is not suitable for implementing the algorithm on the small-scale hardware such as embedded system. In this paper, we propose a boosted random forests in which boosting algorithm is introduced into random forests. Experimental results show that the proposed method, which consists of fewer decision trees, has higher generalization ability comparing to the conventional method. [http://ieeexplore.ieee.org/document/7294983 Boosted random forest | Y. Mishina, M. Tsuchiya, and H. Fujiyoshi - IEEE Xplore]
-<youtube>QHOazyP-YlM</youtube>
+<youtube>9wn1f-30_ZY</youtube>

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 * [[Random Forest (or) Random Decision Forest]]
 * [[XGBoost; eXtreme Gradient Boosted trees]]
 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>

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 * [[Boosting]]
 * [[Random Forest (or) Random Decision Forest]]
-* [[Boosted Random Forest]]
+* [[XGBoost; eXtreme Gradient Boosted trees]]
 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>