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Building a machine learning model normally requires to choose the most informative features from a dataset with hundreds or thousands of features, to maximize model performance, decrease model size and increase explainability.
Challenge: Challenge Selecting a basket of features from a large dataset with lots of features poses an exponential combinatorial problem that may be solved sub-optimally with traditional algorithms.
Potential Benefits of quantum-inspired:
Faster feature selection for large datasets.
Models with better accuracy or robustness.
Improved explainability and storage savings.
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