How to Quantify a Model’s ‘Predictive Integrity’? A Universal Metacognitive Metric for Deep Learning #
Introduction: When Loss and Accuracy Are Not Enough #
In the practice of deep learning, we are accustomed to using Loss and Accuracy to measure the quality of a model. Loss tells us how “wrong” the model is, and accuracy tells us how many it “got right.” These metrics are simple, effective, and the cornerstone of model optimization.
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