Machine Learning for Analysis of Naval Aviator Training

Approved for public release; distribution is unlimited.This project investigated patterns in the training data of Navy aviators in an attempt to predict their success in training. With the help of the sponsor, we assembled a database from many sources of training data. This database covered 18,596 pilot and Naval Flight Officer candidates through their pretesting, classroom instruction, candidate training in generic aircraft, and candidate training in specialized aircraft. This data was a challenge to organize because it had incompatible formats and missing data. After standardizing the formats and fixing errors in the data, and aggregating sparse records to a smaller set of average scores, we had 301 features for the candidates. We then correlated their features using both numeric-correlation and nonnumeric-association (class-characterization) methods. We identified 38 kinds of measures of success in the program and particularly focused on correlations involving those. We did confirm some early indicators of success and failure in the program, but most were not surprising. We conclude that the Navy is doing a good job of identifying candidates likely to be successful.U.S. Fleet Forces CommandNPS-20-N309-B-NR