Difference between revisions of "Tree-based..."

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Revision as of 16:59, 18 December 2018

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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. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium

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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


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