Binary Cross-Entropy

Binary Cross-Entropy

A loss function utilised in binary classification tasks is called binary crossentropy. These are assignments that offer a single, two-option response to a query (yes or no, A or B, 0 or 1, left or right). As in multi-label classification or binary image segmentation, multiple independent such problems can be addressed simultaneously.

According to formal definitions, this loss is equivalent to the mean of the categorical crossentropy loss on numerous two-category tasks.

Loss function introduction
Let's first learn the Loss function before moving on to Log Loss. Consider the following scenario: You believe your machine learning model has identified cats and dogs with success, but how do you know this is the optimal outcome?


Here, we're seeking for measurements or a function that can help us improve the performance of our model. The loss function indicates the accuracy of your model's predictions. Loss will be at its lowest if model projections are closest to the actual values, and at its highest if predictions are farthest from the original values.

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