# What makes binary cross entropy a better choice for binary classification than other loss functions?

I'm reading this post where I came across this quote "Cross-entropy is the default loss function to use for binary classification problems."

But what about it makes it the default and presumably best loss function for binary classification?

Another advantage is that you don't need to think much about how to the define a cost function. The ML framework gives you the cost function right away as long as you have a model that specifies $$P(y|x)$$.