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gptkbp:instanceOf
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gptkb:model
ensemble learning method
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gptkbp:advantage
|
can be computationally intensive
less interpretable than single decision trees
robust to noise
works with categorical and numerical data
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gptkbp:basedOn
|
decision trees
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gptkbp:category
|
supervised learning
non-parametric method
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gptkbp:citation
|
gptkb:Breiman,_L._(2001)._Random_Forests._Machine_Learning,_45(1),_5-32.
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gptkbp:developedBy
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gptkb:Leo_Breiman
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gptkbp:handles
|
high-dimensional data
missing values
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|
https://www.w3.org/2000/01/rdf-schema#label
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Random Forests
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gptkbp:hyperparameter
|
maximum tree depth
minimum samples per leaf
number of features per split
number of trees
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gptkbp:implementedIn
|
gptkb:Spark_MLlib
gptkb:Weka
gptkb:scikit-learn
R
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|
gptkbp:improves
|
prediction accuracy
|
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gptkbp:introducedIn
|
2001
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gptkbp:provides
|
feature importance
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gptkbp:reduces
|
overfitting
|
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gptkbp:relatedTo
|
gptkb:AdaBoost
gradient boosting
bagging classifier
|
|
gptkbp:technique
|
bagging
|
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gptkbp:usedFor
|
gptkb:dictionary
regression
feature selection
|
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gptkbp:bfsParent
|
gptkb:Journal_of_Machine_Learning_Research
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gptkbp:bfsLayer
|
5
|