Difference between revisions of "Random Forest (or) Random Decision Forest"
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| + | [http://www.youtube.com/results?search_query=Random+Forest+Decision+artificial+intelligence YouTube search...] | ||
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| + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]] | ||
** [[...predict categories]] | ** [[...predict categories]] | ||
| − | * [[ | + | * [[Boosted Random Forest]] |
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| + | 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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| + | 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] | ||
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| + | <youtube>J4Wdy0Wc_xQ</youtube> | ||
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| + | <youtube>D_2LkhMJcfY</youtube> | ||
| + | <youtube>QHOazyP-YlM</youtube> | ||
| − | + | == Two-Class Decision Forest == | |
| − | + | * [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-decision-forest Two-Class Decision Forest | Microsoft] | |
| − | <youtube> | + | <youtube>3uqJBa5DTjM</youtube> |
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Latest revision as of 22:53, 5 March 2024
YouTube search... ...Google search
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
- Boosted 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. The Random Forest Algorithm | Niklas Donges @ Towards Data Science
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. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium
Two-Class Decision Forest
