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gptkbp:instance_of
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gptkb:machine_learning
|
|
gptkbp:analyzes
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Tree Structures
|
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gptkbp:based_on
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gptkb:Decision_Trees
|
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gptkbp:can_be_combined_with
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Ensemble Methods
Weak Learners
|
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gptkbp:can_be_used_with
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Structured Data
Unstructured Data
|
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gptkbp:can_handle
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Missing Values
|
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gptkbp:challenges
|
Interpret
|
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gptkbp:developed_by
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Friedman
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https://www.w3.org/2000/01/rdf-schema#label
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Gradient Boosted Trees
|
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gptkbp:improves
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Model Accuracy
|
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gptkbp:is_enhanced_by
|
Regularization
Feature Selection
Feature Scaling
|
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gptkbp:is_evaluated_by
|
Cross-Validation
F1 Score
Mean Squared Error
AUC-ROC
R-squared
Precision-Recall
|
|
gptkbp:is_implemented_in
|
gptkb:XGBoost
gptkb:Cat_Boost
gptkb:Light_GBM
|
|
gptkbp:is_popular_for
|
Winning Data Science Competitions
|
|
gptkbp:is_popular_in
|
Kaggle Competitions
|
|
gptkbp:is_recommended_by
|
gptkb:Support_Vector_Machines
gptkb:neural_networks
Linear Models
|
|
gptkbp:is_scalable
|
Large Datasets
|
|
gptkbp:is_subject_to
|
Bias-Variance Tradeoff
|
|
gptkbp:is_used_in
|
gptkb:E-commerce
gptkb:Natural_Language_Processing
gptkb:advertising
Anomaly Detection
Credit Scoring
Finance
Fraud Detection
Healthcare
Image Classification
Recommendation Systems
Customer Segmentation
Churn Prediction
Time Series Forecasting
|
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gptkbp:reduces
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Overfitting
|
|
gptkbp:requires
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gptkb:Hyperparameter_Tuning
Feature Engineering
|
|
gptkbp:runs_through
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gptkb:Yes
|
|
gptkbp:security
|
Noise
|
|
gptkbp:sensitivity
|
gptkb:Outliers
|
|
gptkbp:speed
|
gptkb:Random_Forests
|
|
gptkbp:suitable_for
|
High-Dimensional Data
|
|
gptkbp:tuning
|
Grid Search
Random Search
Bayesian Optimization
|
|
gptkbp:used_for
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gptkb:Regression
Classification
|
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gptkbp:bfsParent
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gptkb:Tensor_Flow_Decision_Forests
|
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gptkbp:bfsLayer
|
5
|