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gptkbp:instance_of
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gptkb:machine_learning
|
|
gptkbp:based_on
|
Boosting
|
|
gptkbp:can_be_combined_with
|
Weak Learners
|
|
gptkbp:can_handle
|
Missing Values
|
|
gptkbp:challenges
|
gptkb:Hyperparameter_Tuning
Model Complexity
Overfitting
Computational Cost
|
|
gptkbp:developed_by
|
Friedman
|
|
gptkbp:has
|
Learning Rate
Number of Estimators
Subsample Rate
Max Depth
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Gradient Boosting Machines
|
|
gptkbp:improves
|
Model Accuracy
|
|
gptkbp:is_compared_to
|
gptkb:Support_Vector_Machines
gptkb:Random_Forests
gptkb:neural_networks
gptkb:Ada_Boost
|
|
gptkbp:is_evaluated_by
|
Cross-Validation
Confusion Matrix
ROC Curve
Precision-Recall Curve
|
|
gptkbp:is_implemented_in
|
gptkb:XGBoost
gptkb:Cat_Boost
gptkb:Light_GBM
gptkb:Scikit-learn
|
|
gptkbp:is_known_for
|
Flexibility
High Performance
Interpretability
Feature Importance
Handling Non-linearity
|
|
gptkbp:is_popular_among
|
gptkb:researchers
Data Scientists
Statisticians
Machine Learning Practitioners
|
|
gptkbp:is_popular_in
|
Kaggle Competitions
|
|
gptkbp:is_used_in
|
gptkb:E-commerce
gptkb:Natural_Language_Processing
gptkb:advertising
Finance
Healthcare
Image Processing
|
|
gptkbp:reduces
|
Bias
Variance
|
|
gptkbp:requires
|
Tuning of Hyperparameters
|
|
gptkbp:runs_through
|
gptkb:Yes
|
|
gptkbp:sensitivity
|
gptkb:Outliers
|
|
gptkbp:used_for
|
gptkb:Regression
Classification
|
|
gptkbp:uses
|
gptkb:Decision_Trees
|
|
gptkbp:bfsParent
|
gptkb:AT&_T_Bell_Laboratories
|
|
gptkbp:bfsLayer
|
5
|