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Random forest

Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the output is the average of the predictions of…

History, Overview & Algorithm

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Overview

History

Algorithm

Properties

Variants

Kernel random forest

Disadvantages

Advanced semantic analysis

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Map overview Semantic statistics

Random forest

Nodes52
Edges51
Triples105
Avg. degree1.96
Density0.038462
Components1

How this topic connects Entity context

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Random forest

Top relations

related to history · 23
Random forest → Amit, Breiman's, Davies, Dietterich, Finally, Geman, Ghahramani, He, Heath, Ho, Jeon, KeRF, KeRFs Centered KeRF, Kernel Random Forest, Kleinberg's, Leo Breiman, Lin, Salzberg, Scornet, The
related to External links · 11
Random forest → Andy, Classification, Discussion, Leo Breiman's, Liaw, Matthew, News, Random Forests, Regression, Vol, Wiener
related to Random forests for high-dimensional data · 11
Random forest → Eliminate, Enriched Random Forest, ERF, Give, Prefiltering, Some, The, This, Tree-weighted, TWRF, Use
related to Relationship to nearest neighbors · 9
Random forest → Both, For, Here, In, Jeon, Lin, NN, These, Weight
related to Disadvantages · 8
Random forest → Another, Decision, For, It, Likewise, This, To, While
related to ExtraTrees · 8
Random forest → Adding, As, Default, ExtraTrees, Gini, Similar, The, Then
related to From bagging to random forests · 8
Random forest → An, For, Ho, In, Random, The, This, Typically
related to Preliminaries: decision tree learning · 7
Random forest → Decision, Hastie, However, In, Random, This, Tree
related to Bagging · 6
Random forest → Given, Sample, The, Train, Xb, Yb
related to Unsupervised learning · 4
Random forest → Addcl, As, One, Random

Important terminology Word statistics

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Important terminology

random trees forest displaystyle forests tree features training decision mathbf feature regression frac sum classification bagging set algorithm variable importance

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Random forestis aclass selected by most trees0.90text
Random forestrelated to BaggingThe0.60section
Random forestrelated to BaggingGiven0.60section
Random forestrelated to BaggingSample0.60section
Random forestrelated to BaggingXb0.60section
Random forestrelated to BaggingYb0.60section
Random forestrelated to BaggingTrain0.60section
Random forestrelated to DisadvantagesWhile0.60section
Random forestrelated to DisadvantagesDecision0.60section
Random forestrelated to DisadvantagesThis0.60section
Random forestrelated to DisadvantagesIt0.60section
Random forestrelated to DisadvantagesFor0.60section

Related concept clusters Concept neighborhoods

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    Connections between topic areas Semantic bridges

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    Min side: 3
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