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(Created page with "[http://www.youtube.com/results?search_query=Decision+Tree+Regression YouTube search...] * AI Solver * Capabilities the ensemble is a collection of models that do n...")
 
 
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[http://www.youtube.com/results?search_query=Decision+Tree+Regression YouTube search...]
 
[http://www.youtube.com/results?search_query=Decision+Tree+Regression YouTube search...]
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[http://www.google.com/search?q=Decision+Tree+Regression+machine+learning+ML+artificial+intelligence ...Google search]
  
 
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the ensemble is a collection of models that do not predict the real objective field of the ensemble, but rather the improvements needed for the function that computes this objective. As shown in the image above, the modeling process starts by assigning some initial values to this function, and creates a model to predict which gradient will improve the function results. The next iteration considers both the initial values and these corrections as its original state, and looks for the next gradient to improve the prediction function results even further. The process stops when the prediction function results match the real values or the number of iterations reaches a limit. As a consequence, all the models in the ensemble will always have a numeric objective field, the gradient for this function. The real objective field of the problem will then be computed by adding up the contributions of each model weighted by some coefficients. If the problem is a classification, each category (or class) in the objective field has its own subset of models in the ensemble whose goal is adjusting the function to predict this category. [http://blog.bigml.com/2017/03/14/introduction-to-boosted-trees/ Introduction to Boosted Trees | bigML]
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Decision Tree algorithms categorize the population for several sets based on some chosen properties (independent variables) of a population. Usually, this algorithm is used to solve classification problems. Categorization is done by using some techniques such as Gini, Chi-square, entropy etc. This decision tree can be further extended by identifying suitable properties to define more categories. [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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http://ai2-s2-public.s3.amazonaws.com/figures/2017-08-08/16533dca42cafce4b00d224727dc5d977ef7d67e/8-Figure3-1.png
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Decision forests (regression, two-class, and multi class), decision jungles (two-class and multi class), and boosted decision trees (regression and two-class) are all based on decision trees, a foundation machine learning concept. There are many variants of decision trees, but they all do the same thing—subdivide the feature space into regions with mostly the same label. These can be regions of consistent category or of constant value, depending on whether you are doing classification or regression. - Dinesh Chandrasekar
  
https://littleml.files.wordpress.com/2017/03/boosted-trees-process.png?w=497
 
  
 
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http://msdnshared.blob.core.windows.net/media/TNBlogsFS/prod.evol.blogs.technet.com/CommunityServer.Blogs.Components.WeblogFiles/00/00/01/02/52/AlgoDecisionTree-2.png

Latest revision as of 13:29, 2 February 2019

YouTube search... ...Google search

Decision Tree algorithms categorize the population for several sets based on some chosen properties (independent variables) of a population. Usually, this algorithm is used to solve classification problems. Categorization is done by using some techniques such as Gini, Chi-square, entropy etc. This decision tree can be further extended by identifying suitable properties to define more categories. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium

8-Figure3-1.png

Decision forests (regression, two-class, and multi class), decision jungles (two-class and multi class), and boosted decision trees (regression and two-class) are all based on decision trees, a foundation machine learning concept. There are many variants of decision trees, but they all do the same thing—subdivide the feature space into regions with mostly the same label. These can be regions of consistent category or of constant value, depending on whether you are doing classification or regression. - Dinesh Chandrasekar


AlgoDecisionTree-2.png