Properties (54)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:Company
|
| gptkbp:basedOn |
Decision Trees
|
| gptkbp:canBe |
Yes
|
| gptkbp:developedBy |
Friedman_et_al.
|
| gptkbp:has |
Learning Rate
Number of Estimators Regularization Parameters Subsample Rate Max_Depth |
| https://www.w3.org/2000/01/rdf-schema#label |
Gradient Boosting Machines
|
| gptkbp:improves |
Model_Accuracy
|
| gptkbp:isAvenueFor |
Ensemble Learning
|
| gptkbp:isEvaluatedBy |
Cross-Validation
F1 Score Confusion Matrix ROC_Curve Precision-Recall Curve |
| gptkbp:isFacilitatedBy |
Missing Values
|
| gptkbp:isLocatedIn |
gptkb:LightGBM
XGBoost CatBoost |
| gptkbp:isPartOf |
gptkb:Support_Vector_Machines
gptkb:Random_Forests gptkb:AdaBoost Neural Networks |
| gptkbp:isPopularFor |
Winning_Data_Science_Competitions
|
| gptkbp:isPopularIn |
Kaggle Competitions
|
| gptkbp:isStudiedIn |
Outliers
|
| gptkbp:isTrainedIn |
Grid Search
Bayesian Optimization Random_Search |
| gptkbp:isUsedFor |
Large Datasets
Feature Importance Partial Dependence Plots SHAP Values Weak Learners |
| gptkbp:isUsedIn |
gptkb:Recommendation_Systems
Finance Fraud Detection Healthcare Image Processing Marketing Natural Language Processing Risk Assessment Predictive Modeling Customer Segmentation Churn Prediction Time_Series_Forecasting |
| gptkbp:reduces |
Bias
Variance |
| gptkbp:requires |
Hyperparameter_Tuning
|
| gptkbp:usedFor |
Classification
Regression |
| gptkbp:uses |
Gradient Descent
|