Measuring model loss
We saw earlier how we could optimize parameters by minimizing some distance function, D. This distance function, also called the loss function, is the performance measure by which we evaluate possible functions. In machine learning, a loss function measures how bad the model performs. A high loss function goes hand in hand with low accuracy, whereas if the function is low, then the model is doing well.
In this case, our issue is a binary classification problem. Because of that, we will be using the binary cross-entropy loss, as we can see in the following formula:
Let's go through this formula step by step:
- DBCE: This is the distance ...