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]
  
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Linear regression is a common statistical method, which has been adopted in machine learning and enhanced with many new methods for fitting the line and measuring error. In the most basic sense, regression refers to prediction of a numeric target. Linear regression is still a good choice when you want a very simple model for a basic predictive task. Linear regression also tends to work well on high-dimensional, sparse data sets lacking complexity. Azure Machine Learning Studio supports a variety of regression models, in addition to linear regression. However, the term "regression" can be interpreted loosely, and some types of regression provided in other tools are not supported in Studio.
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== Two-Class Decision Forest ==
 
== Two-Class Decision Forest ==
  
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* [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-decision-forest Two-Class Decision Forest | Microsoft]
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* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-decision-forest Two-Class Decision Forest | Microsoft]
  
 
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Revision as of 21:24, 3 June 2018

YouTube search...

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. The Random Forest Algorithm | Niklas Donges @ Towards Data Science

0*tG-IWcxL1jg7RkT0.png


Two-Class Decision Forest

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