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gptkbp:instance_of
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gptkb:machine_learning
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gptkbp:analyzes
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Flowchart
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gptkbp:based_on
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Tree Structure
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gptkbp:benefits
|
Easy to interpret
Handles both numerical and categorical data
Prone to overfitting
Sensitive to noisy data
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gptkbp:can_be_combined_with
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gptkb:Random_Forests
Boosting
Bagging
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|
gptkbp:can_be_used_in
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gptkb:Risk_Management
Predictive Modeling
Customer Segmentation
|
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gptkbp:can_handle
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Missing Values
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gptkbp:created_by
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gptkb:J._Ross_Quinlan
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gptkbp:evaluates
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Accuracy
F1 Score
Precision
Recall
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gptkbp:first_introduced
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gptkb:1986
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gptkbp:has_programs
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Supervised Learning
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|
https://www.w3.org/2000/01/rdf-schema#label
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Decision Trees
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gptkbp:input_output
|
Continuous Values
Discrete Values
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|
gptkbp:is_enhanced_by
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Pruning
Ensemble Methods
|
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gptkbp:is_evaluated_by
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Cross-Validation
Confusion Matrix
ROC Curve
AUC Score
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gptkbp:is_implemented_in
|
gptkb:Java
gptkb:C++
gptkb:Python
gptkb:R
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|
gptkbp:is_often_compared_to
|
gptkb:Support_Vector_Machines
gptkb:Logistic_Regression
gptkb:neural_networks
k-Nearest Neighbors
|
|
gptkbp:is_often_used_in
|
gptkb:advertising
Finance
Healthcare
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gptkbp:is_popular_in
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gptkb:Artificial_Intelligence
Data Mining
Academic Research
Industry Applications
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gptkbp:requires
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Training Data
Feature Selection
|
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gptkbp:suitable_for
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High-Dimensional Data
Unbalanced Datasets
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gptkbp:used_for
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gptkb:Regression
Classification
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gptkbp:bfsParent
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gptkb:machine_learning
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gptkbp:bfsLayer
|
3
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