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gptkbp:instanceOf
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gptkb:model
gptkb:ensemble_learning_method
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gptkbp:advantage
|
robust to outliers
can be slow with many trees
estimates feature importance
handles high dimensional data
large memory usage
less interpretable
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|
gptkbp:basedOn
|
gptkb:tree
|
|
gptkbp:citation
|
gptkb:Breiman,_L._(2001)._Random_Forests._Machine_Learning,_45(1),_5-32.
|
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gptkbp:developedBy
|
gptkb:Leo_Breiman
|
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gptkbp:hyperparameter
|
number of trees
max depth
max features
min samples split
|
|
gptkbp:implementedIn
|
gptkb:Spark_MLlib
gptkb:scikit-learn
R
|
|
gptkbp:introducedIn
|
2001
|
|
gptkbp:output
|
majority vote
average prediction
|
|
gptkbp:reduces
|
overfitting
|
|
gptkbp:relatedTo
|
gptkb:AdaBoost
gradient boosting
bagging classifier
|
|
gptkbp:usedFor
|
gptkb:dictionary
regression
feature selection
|
|
gptkbp:uses
|
bagging
random feature selection
|
|
gptkbp:bfsParent
|
gptkb:Classification_and_Regression_Tree
gptkb:C&RT
gptkb:Bagging
gptkb:Gradient_Boosting
gptkb:Text_Classification
|
|
gptkbp:bfsLayer
|
7
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Random Forest
|