Statements (72)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:chamber_music
|
| gptkbp:can_be_combined_with |
multiple decision trees
|
| gptkbp:can_be_tuned_using |
grid search
|
| gptkbp:can_be_used_for |
feature selection
customer segmentation anomaly detection text classification |
| gptkbp:can_be_used_to |
analyze customer behavior
evaluate marketing strategies optimize marketing campaigns forecast sales assess credit risk identify fraud predict customer churn predict disease outcomes predict employee turnover predict loan defaults predict stock prices |
| gptkbp:can_be_visualized |
using tree diagrams
|
| gptkbp:can_handle |
missing values
high dimensional data |
| gptkbp:developed_by |
gptkb:Leo_Breiman
|
| https://www.w3.org/2000/01/rdf-schema#label |
Random Forests
|
| gptkbp:improves |
single decision trees
|
| gptkbp:input_output |
probability estimates
|
| gptkbp:introduced_in |
gptkb:2001
|
| gptkbp:is_applicable_to |
both categorical and continuous data
|
| gptkbp:is_based_on |
bagging technique
|
| gptkbp:is_effective_against |
cross-validation
imbalanced datasets |
| gptkbp:is_implemented_in |
gptkb:R_programming_language
gptkb:scikit-learn |
| gptkbp:is_less_interpretable_than |
single decision trees
|
| gptkbp:is_less_prone_to |
overfitting than decision trees
|
| gptkbp:is_part_of |
data mining techniques
|
| gptkbp:is_popular_for |
Kaggle competitions
|
| gptkbp:is_robust_to |
outliers
|
| gptkbp:is_used_in |
gptkb:sports_team
finance natural language processing real estate valuation supply chain optimization bioinformatics image classification healthcare analytics transportation modeling environmental modeling social media analysis marketing analytics energy consumption forecasting telecommunications analysis |
| gptkbp:provides |
feature importance
|
| gptkbp:reduces |
overfitting
|
| gptkbp:requires |
hyperparameter tuning
|
| gptkbp:runs_through |
for faster computation
|
| gptkbp:sensitivity |
noisy data
|
| gptkbp:suitable_for |
real-time predictions
|
| gptkbp:technique |
data analysis
data-driven decision making risk assessment predictive modeling data classification ensemble learning statistical learning |
| gptkbp:type_of |
gptkb:machine_learning
supervised learning algorithm |
| gptkbp:used_for |
gptkb:Biology
regression |
| gptkbp:uses |
bootstrap aggregating
|
| gptkbp:works_with |
large datasets
|
| gptkbp:bfsParent |
gptkb:machine_learning
|
| gptkbp:bfsLayer |
3
|