Finance

Supply Chain

Data Analysis

Feature Selection

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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