Properties (62)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:Architect
|
| gptkbp:aimsTo |
prediction accuracy
|
| gptkbp:basedOn |
bootstrap aggregating
|
| gptkbp:can_be |
for faster computation
|
| gptkbp:composedOf |
multiple decision trees
|
| gptkbp:evaluates |
high-dimensional spaces
|
| https://www.w3.org/2000/01/rdf-schema#label |
Random Forests
|
| gptkbp:is_a |
supervised learning algorithm
|
| gptkbp:is_available_in |
tree structures
|
| gptkbp:is_designed_to |
outliers
|
| gptkbp:is_evaluated_by |
cross-validation
single decision trees |
| gptkbp:is_popular_among |
data science
|
| gptkbp:is_recognized_for |
MATLAB
R scikit-learn |
| gptkbp:is_used_in |
healthcare
finance marketing urban planning environmental monitoring geospatial analysis natural language processing network security quality control risk assessment supply chain optimization bioinformatics feature selection customer segmentation climate modeling credit scoring fraud detection image classification predictive maintenance real-time predictions sales forecasting video analysis time series forecasting classification anomaly detection image segmentation sports analytics transportation modeling regression social media analysis text classification telecommunications analysis customer churn prediction e-commerce recommendations energy consumption prediction |
| gptkbp:isFacilitatedBy |
missing values
categorical variables |
| gptkbp:isUsedFor |
other algorithms
|
| gptkbp:powerOutput |
probability estimates
|
| gptkbp:produces |
gptkb:Leo_Breiman
|
| gptkbp:provides |
feature importance
|
| gptkbp:reduces |
overfitting
|
| gptkbp:requires |
hyperparameter tuning
|
| gptkbp:sensors |
class imbalance
|
| gptkbp:suitableFor |
large datasets
|
| gptkbp:uses |
bagging technique
|