File:USING MACHINE LEARNING TO PREDICT EARLY SERVICE SEPARATION OF TECHNICAL AND NON-TECHNICAL SAILORS (IA usingmachinelear1094564123).pdf
Original file (1,275 × 1,650 pixels, file size: 2.44 MB, MIME type: application/pdf, 112 pages)
Captions
Captions
Summary[edit]
USING MACHINE LEARNING TO PREDICT EARLY SERVICE SEPARATION OF TECHNICAL AND NON-TECHNICAL SAILORS
(   ) |
|
|---|---|
| Author |
Cole, Stephen |
| Title |
USING MACHINE LEARNING TO PREDICT EARLY SERVICE SEPARATION OF TECHNICAL AND NON-TECHNICAL SAILORS |
| Publisher |
Monterey, CA; Naval Postgraduate School |
| Description |
Sailors are difficult to recruit, expensive to train, and hard to retain. This is particularly true in the technical sailor community. Retention of both technical and non-technical sailors is critical to future manning continuity and capability within the Royal Australian Navy. This research employs machine learning to analyze Royal Australian Navy exit survey data collected between 1999 and 2018 to better predict the attitudes and behaviors of a sailor voluntarily separating between four and eight years of service. Furthermore, this study analyzes in particular whether technical sailors behave differently compared to non-technical sailors. In comparison to traditional modeling techniques, the analysis finds that machine learning can more accurately detect differences in the attitudes and behaviors of technical and non-technical sailors when they are deciding to voluntarily separate from service. Furthermore, the analysis can identify differences in sentiment across periods of time covering key career milestones. This analysis and its findings may now be employed to analyze specific critical target groups in both the Royal Australian Navy technical and non-technical sailor communities to understand their attitudes and behaviors, and help support current and future sailor retention policy initiatives. Subjects: machine learning; regression; retention; sailor; technical sailor; non-technical sailor |
| Language | English |
| Publication date | December 2019 |
| Current location |
IA Collections: navalpostgraduateschoollibrary; fedlink |
| Accession number |
usingmachinelear1094564123 |
| Source | |
| Permission (Reusing this file) |
Copyright is reserved by the copyright owner. |
Licensing[edit]
|
This work is in the public domain in the United States because it is a work prepared by an officer or employee of the United States Government as part of that person’s official duties under the terms of Title 17, Chapter 1, Section 105 of the US Code.
Note: This only applies to original works of the Federal Government and not to the work of any individual U.S. state, territory, commonwealth, county, municipality, or any other subdivision. This template also does not apply to postage stamp designs published by the United States Postal Service since 1978. (See § 313.6(C)(1) of Compendium of U.S. Copyright Office Practices). It also does not apply to certain US coins; see The US Mint Terms of Use.
|
 |
| This file has been identified as being free of known restrictions under copyright law, including all related and neighboring rights. | ||
File history
Click on a date/time to view the file as it appeared at that time.
| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 20:01, 25 July 2020 |  | 1,275 × 1,650, 112 pages (2.44 MB) | Fæ (talk | contribs) | FEDLINK - United States Federal Collection usingmachinelear1094564123 (User talk:Fæ/IA books#Fork8) (batch 1993-2020 #31663) |
You cannot overwrite this file.
File usage on Commons
The following page uses this file: