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Approximating XGBoost with an interpretable decision tree
Omer Sagi,
Lior Rokach
Department of Software and Information Systems Engineering
Research output
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Contribution to journal
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Article
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peer-review
192
Scopus citations
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Keyphrases
Performance Prediction
100%
Decision Tree
100%
XGBoost
100%
Gradient Boosting Decision Tree
100%
Decision Tree Model
50%
Decision Forest
50%
Black-box Model
25%
Healthcare
25%
Model Output
25%
Machine Learning
25%
Machine Learning Models
25%
Interpretable Machine Learning
25%
XGBoost Model
25%
Decision Tree Method
25%
Kaggle
25%
Computer Science
Decision Trees
100%
Extreme Gradient Boosting
100%
Predictive Performance
80%
Gradient Boosting
80%
Machine Learning
40%
Decision Tree Model
40%
Interpretability
20%
Interpretable Machine Learning
20%
Learning Practitioner
20%
Chemical Engineering
Learning System
100%
Economics, Econometrics and Finance
Machine Learning
100%