Statements (34)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
ensemble learning method |
| gptkbp:advantage |
robust to outliers
can be slow with many trees estimates feature importance handles high dimensional data large memory usage less interpretable |
| gptkbp:basedOn |
gptkb:tree
|
| gptkbp:citation |
gptkb:Breiman,_L._(2001)._Random_Forests._Machine_Learning,_45(1),_5-32.
|
| gptkbp:developedBy |
gptkb:Leo_Breiman
|
| https://www.w3.org/2000/01/rdf-schema#label |
Random Forest
|
| 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:Text_Classification
gptkb:Supervised_learning |
| gptkbp:bfsLayer |
6
|