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Within machine learning model evaluation regimes, feature selection is atechnique to reduce model complexity and improve model performance in regardsto generalization, model fit, and accuracy of prediction. However, the searchover the space of features to find the subset of $k$ optimal features is aknown NP-Hard problem. In this work, we study metrics for encoding thecombinatorial search as a binary quadratic model, such as Generalized MeanInformation Coefficient and Pearson Correlation Coefficient in application tothe underlying regression problem of price prediction. We investigatetrade-offs in the form of run-times and model performance, of leveragingquantum-assisted vs. classical subroutines for the combinatorial search, usingminimum redundancy maximal relevancy as the heuristic for our approach. Weachieve accuracy scores of 0.9 (in the range of [0,1]) for finding optimalsubsets on synthetic data using a new metric that we define. We test andcross-validate predictive models on a real-world problem of price prediction,and show a performance improvement of mean absolute error scores for ourquantum-assisted method $(1471.02 \pm{135.6})$, vs. similar methodologies suchas recursive feature elimination $(1678.3 \pm{143.7})$. Our findings show thatby leveraging quantum-assisted routines we find solutions that increase thequality of predictive model output while reducing the input dimensionality tothe learning algorithm on synthetic and real-world data.

Quantum-Assisted Feature Selection for Vehicle Price Prediction Modeling