Statements (49)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
ensemble learning method |
| gptkbp:advantage |
parallelizable
can be slow with many trees large memory usage handles large datasets less interpretable than single trees |
| gptkbp:canBe |
binary classification
feature selection anomaly detection multi-class classification regression tasks ensemble stacking imputation of missing values ranking feature importance |
| gptkbp:category |
supervised learning
|
| gptkbp:citation |
gptkb:Breiman,_L._(2001)._Random_Forests._Machine_Learning,_45(1),_5-32.
|
| gptkbp:developedBy |
gptkb:Leo_Breiman
|
| gptkbp:featureImportance |
can be estimated
|
| gptkbp:handles |
high-dimensional data
missing values |
| https://www.w3.org/2000/01/rdf-schema#label |
Random Forest Ensemble
|
| gptkbp:hyperparameter |
maximum tree depth
minimum samples per leaf number of features per split number of trees |
| gptkbp:implementedIn |
gptkb:Spark_MLlib
gptkb:Weka gptkb:scikit-learn R |
| gptkbp:improves |
prediction accuracy
|
| gptkbp:introducedIn |
2001
|
| gptkbp:output |
average prediction (regression)
majority vote (classification) |
| gptkbp:reduces |
overfitting
|
| gptkbp:relatedTo |
gptkb:Gradient_Boosting
gptkb:AdaBoost bagging boosting Extra Trees |
| gptkbp:robustTo |
outliers
|
| gptkbp:technique |
bagging
|
| gptkbp:usedFor |
gptkb:dictionary
regression |
| gptkbp:uses |
decision trees
random feature selection bootstrap sampling |
| gptkbp:bfsParent |
gptkb:RFE
|
| gptkbp:bfsLayer |
7
|