Benchmark of quantum-inspired encodings shows they provide no reliable machine-learning advantage over classical methods on classical datasets due to their geometric properties.
Multiclass classification of dry beans using computer vision and machine learning techniques.Computers and Electronics in Agriculture, 174:105507, 2020
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Model evaluation in supervised learning should be treated as a context-dependent, decision-oriented process aligned with operational objectives rather than relying on a small set of aggregate metrics.
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A Matched Spectral Benchmark of Quantum Inspired Feature Maps
Benchmark of quantum-inspired encodings shows they provide no reliable machine-learning advantage over classical methods on classical datasets due to their geometric properties.
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Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Model evaluation in supervised learning should be treated as a context-dependent, decision-oriented process aligned with operational objectives rather than relying on a small set of aggregate metrics.