What does "Binary Cross-Entropy" mean?

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Binary cross-entropy is a way to measure how well a model is doing when it predicts whether something belongs to one of two classes, like yes or no. It helps to understand if the predictions made by the model are close to the actual results.

When a model makes a guess, binary cross-entropy looks at the differences between those guesses and the true outcomes. A lower score means the model is doing a better job, while a higher score indicates that it's making more mistakes. This measurement is commonly used in tasks where the goal is to classify items into two categories.

In sound event detection, for example, binary cross-entropy can help a model learn when sounds are present or absent. By using this measure, the model can be trained to improve its accuracy in identifying sounds, leading to better performance overall.