  This work compares supervised machine learning methods using reliable data
from constructive simulations to estimate the most effective moment for
launching missiles during air combat. We employed resampling techniques to
improve the predictive model, analyzing accuracy, precision, recall, and
f1-score. Indeed, we could identify the remarkable performance of the models
based on decision trees and the significant sensitivity of other algorithms to
resampling techniques. The models with the best f1-score brought values of
0.379 and 0.465 without and with the resampling technique, respectively, which
is an increase of 22.69%. Thus, if desirable, resampling techniques can improve
the model's recall and f1-score with a slight decline in accuracy and
precision. Therefore, through data obtained through constructive simulations,
it is possible to develop decision support tools based on machine learning
models, which may improve the flight quality in BVR air combat, increasing the
effectiveness of offensive missions to hit a particular target.
