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Interpretability
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Methods for Improving the Interpretability and Evaluation of Machine Learning Models and Decision Making Systems
Student Name:
Nizam, Sohail
Date Uploaded:
04/07/2023
Research Field:
Statistics and Computer Science
Department or Specialty:
Biostatistics
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Year
Spring 2023
1
School
Laney Graduate School
1
Department / Specialty
Biostatistics
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Degree
Ph.D.
1
Submission Type
Dissertation
1
Research Field
Computer Science
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Statistics
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Committee
Abeed Sarker, Emory University
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Benjamin Risk, Emory University
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David Benkeser, Emory University
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Razieh Nabi, Emory University
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Keyword
Causal Inference
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Heterogeneous Treatment Effects
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Interpretability
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Machine Learning
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Model Evaluation
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