  Feature selection is a technique in statistical prediction modeling that
identifies features in a record with a strong statistical connection to the
target variable. Excluding features with a weak statistical connection to the
target variable in training not only drops the dimension of the data, which
decreases the time complexity of the algorithm, it also decreases noise within
the data which assists in avoiding overfitting. In all, feature selection
assists in training a robust statistical model that performs well and is
stable. Given the lack of scalability in classical computation, current
techniques only consider the predictive power of the feature and not redundancy
between the features themselves. Recent advancements in feature selection that
leverages quantum annealing (QA) gives a scalable technique that aims to
maximize the predictive power of the features while minimizing redundancy. As a
consequence, it is expected that this algorithm would assist in the
bias/variance trade-off yielding better features for training a statistical
model. This paper tests this intuition against classical methods by utilizing
open-source data sets and evaluate the efficacy of each trained statistical
model well-known prediction algorithms. The numerical results display an
advantage utilizing the features selected from the algorithm that leveraged QA.
