Random Forest

URI: https://gptkb.org/entity/Random_Forest
GPTKB entity
Statements (64)
Predicate Object
gptkbp:instance_of gptkb:musical_group
gptkbp:analyzes tree structures
gptkbp:applies_to healthcare
environmental studies
gptkbp:based_on CART algorithm
gptkbp:can_be_used_with multiple decision trees
gptkbp:controls missing values
categorical variables
gptkbp:developed_by gptkb:Leo_Breiman
gptkbp:example supervised learning
gptkbp:features bootstrapping
https://www.w3.org/2000/01/rdf-schema#label Random Forest
gptkbp:hyper_threading faster computation
gptkbp:input_output probability estimates
gptkbp:is_capable_of outliers
gptkbp:is_effective_against risk assessment
predictive modeling
high-dimensional data
predicting outcomes
classifying imbalanced datasets
gptkbp:is_implemented_in gptkb:MATLAB
gptkb:R_programming_language
gptkb:scikit-learn
gptkbp:is_known_for high accuracy
handling non-linear relationships
gptkbp:is_often_compared_to gradient boosting
gptkbp:is_opposed_by single decision trees
gptkbp:is_popular_in data science
Kaggle competitions
gptkbp:is_used_for gptkb:computer
real estate valuation
network intrusion detection
feature selection
customer segmentation
credit scoring
image classification
recommendation systems
sales forecasting
time series forecasting
anomaly detection
regression
sensitivity analysis
text classification
market basket analysis
data imputation
gptkbp:is_used_in gptkb:sports_team
image processing
finance
bioinformatics
social sciences
marketing analytics
gptkbp:is_vulnerable_to overfitting than individual trees
gptkbp:provides feature importance
gptkbp:reduces overfitting
gptkbp:requires hyperparameter tuning
gptkbp:sensor class imbalance
gptkbp:speed single decision trees
gptkbp:suitable_for large datasets
multi-class classification
gptkbp:type_of gptkb:software_framework
ensemble method
gptkbp:uses bagging
gptkbp:bfsParent gptkb:microprocessor
gptkbp:bfsLayer 3