[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120007-en":3,"doc-seo-120007-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120007,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Predicting Success of Pilot Training Candidates Using Interpretable Machine Learning - Thesis","The United States Air Force faces persistent challenges in sustaining pilot access, driven by both attrition of experienced pilots and washout rates during training. This thesis examines pilot training attrition by improving the selection process for pilot candidates. Using historical specialized undergraduate pilot training (SUPT) data, it applies interpretable machine learning to identify factors linked to SUPT success and to provide decision justifications. Three interpretable models are evaluated, with PCSM score found as the strongest predictor. The top model reaches an F1 score of 0.93, demonstrating how interpretable methods can support trustworthy selection decisions.","Air Force Institute of Technology  \nAFIT Scholar  \n\n| Theses and Dissertations | Student Graduate Works |\n| --- | --- |\n| 3-2023\u003Cbr>Predicting Success of Pilot Training Candidates Using Interpretable Machine Learning\u003Cbr>Alexandra S. King\u003Cbr>Follow this and additional works at: [https://scholar.afit.edu/etd](https://scholar.afit.edu/etd)\u003Cbr> Part of the Artificial Intelligence and Robotics Commons, and the Aviation and Space Education Commons |  |\n\nRecommended Citation  \nKing, Alexandra S., \"Predicting Success of Pilot Training Candidates Using Interpretable Machine Learning\" (2023) . Theses and Dissertations. 7002.  \n[https://scholar.afit.edu/etd/7002](https://scholar.afit.edu/etd/7002)  \nThis Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please [contact AFIT.ENWL.Repository@us.af.mil](contact AFIT.ENWL.Repository@us.af.mil).  \nPREDICTING SUCCESS OF PILOT  \nTRAINING CANDIDATES USING  \nINTERPRETABLE MACHINE LEARNING  \nTHESIS  \nAlexandra S. King, Second Lieutenant, USAFAFIT-ENS-MS-23-M-134  \nDEPARTMENT OF THE AIR FORCE  \nAIR UNIVERSITY  \nAIR FORCE INSTITUTE OF TECHNOLOGY  \nWright-Patterson Air Force Base, Ohio  \nDISTRIBUTION STATEMENT A  \nAPPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.  \nThe views expressed in this document are those of the author and do not reflect the official policy or position of the United States Air Force, the United States Department of Defense or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States.  \nAFIT-ENS-MS-23-M-134  \nPREDICTING SUCCESS OF PILOT TRAINING CANDIDATES USING INTERPRETABLE MACHINE LEARNING  \nTHESIS  \nPresented to the Faculty  \nDepartment of Operations Research  \nGraduate School of Engineering and Management  \nAir Force Institute of Technology  \nAir University  \nAir Education and Training Command  \nin Partial Fulfillment of the Requirements for the  \nDegree of Master of Science in Operations Research  \nAlexandra S. King, B.S.O.R.  \nSecond Lieutenant, USAF  \nMarch 23, 2023  \nDISTRIBUTION STATEMENT A  \nAPPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.  \nAFIT-ENS-MS-23-M-134  \nPREDICTING SUCCESS OF PILOT TRAINING CANDIDATES USING INTERPRETABLE MACHINE LEARNING  \nTHESIS  \nAlexandra S. King, B.S.O.R.  \nSecond Lieutenant, USAF  \nCommittee Membership:  \nMaj. Michael J. Garee, Ph.D  \nChair  \nBrian J. Lunday, Ph.D  \nMember  \nMaj. William N. Caballero, Ph.D  \nMember  \nAFIT-ENS-MS-23-M-134  \nAbstract  \nThe United States Air Force (USAF) has struggled with a sustained pilot shortage over the past several years; senior military and government leaders have been working towards a solution to the problem, with no noticeable improvements. Both attrition of more experienced pilots as well as wash out rates within pilot training contribute to this issue. This research focuses on pilot training attrition. Improving the process for selecting pilot candidates can reduce the number of candidates who fail. This research uses historical specialized undergraduate pilot training (SUPT) data and leverages select machine learning techniques to determine which factors are associated with success in SUPT. Humanly understandable (known as interpretable) machine learning techniques will be used to predict SUPT outcome, as these models provide justifications for these predictions and build trust with decision-makers. Three interpretable models were considered, including two rule-based models and one tree-based model. PCSM score was identified as the strongest predictor for success. The best performing model achieved an F1 score of 0.93, compared to 0.84 and 0.77 for the other models. The results of this research emphasizes the usefulness of interpretable models and their ability to inform a decision-maker, assisting them in their selecti","cbCaitD076PYdyww","https://ap.wps.com/l/cbCaitD076PYdyww","pdf",1387278,1,59,"English","en",105,"# Abstract\n# Introduction\n## Motivation for Research\n## Background\n## Problem Statement\n## Research Objectives\n## Scope\n## Summary of Key Contributions\n## Overview\n# Background and Literature Review\n## Pilot Training Studies\n## Explainable AI Methods\n## Interpretability and Its Importance\n## Studies Using Interpretable Models\n## Comparing Modern Interpretable Models\n## CORELS Example Output\n# Methodology\n## Data\n### Structure of the Data and Initial Cleaning\n### Preliminary Data Analysis\n### SMOTE\n## Software\n## Assumptions/Limitations\n## Interpretable Methods Chosen","[{\"question\":\"What problem does the thesis address in pilot training?\",\"answer\":\"It addresses sustained pilot shortages caused by attrition of experienced pilots and washout rates during pilot training, with a focus on training attrition.\"},{\"question\":\"How does the research predict SUPT success?\",\"answer\":\"It uses historical SUPT data and applies interpretable machine learning models to determine factors associated with success and to provide human-understandable justifications.\"},{\"question\":\"Which factor is identified as the strongest predictor?\",\"answer\":\"The PCSM score is identified as the strongest predictor for SUPT success.\"}]","Predicting Success of Pilot Training Candidates Using Interpretable Machine Learning - 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