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gptkbp:instance_of
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gptkb:software_framework
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gptkbp:bfsLayer
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4
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gptkbp:bfsParent
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gptkb:Tensor_Flow_Decision_Forests
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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_used_with
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Structured Data
Unstructured Data
Ensemble Methods
Weak Learners
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gptkbp:challenges
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Interpret
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gptkbp:controls
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Missing Values
|
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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
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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
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gptkb:Cat_Boost
gptkb:Light_GBM
XG Boost
|
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gptkbp:is_popular_in
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Kaggle Competitions
Winning Data Science Competitions
|
|
gptkbp:is_scalable
|
Large Datasets
|
|
gptkbp:is_subject_to
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Bias-Variance Tradeoff
|
|
gptkbp:is_used_for
|
gptkb:Regression
Classification
|
|
gptkbp:is_used_in
|
gptkb:film_production_company
gptkb:software
gptkb:shopping_mall
Anomaly Detection
Credit Scoring
Finance
Fraud Detection
Healthcare
Image Classification
Recommendation Systems
Customer Segmentation
Churn Prediction
Time Series Forecasting
|
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gptkbp:passes_through
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gptkb:battle
|
|
gptkbp:reduces
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Overfitting
|
|
gptkbp:requires
|
gptkb:software
Feature Engineering
|
|
gptkbp:security_features
|
Noise
|
|
gptkbp:sensor
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gptkb:Outliers
|
|
gptkbp:speed
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gptkb:Random_Forests
|
|
gptkbp:suitable_for
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gptkb:Support_Vector_Machines
gptkb:microprocessor
High-Dimensional Data
Linear Models
|
|
gptkbp:tuning
|
Grid Search
Random Search
Bayesian Optimization
|