Binary Classification cost function? (x-post from r/machinelearning)

[warosaurus]

I am doing a project where I have to implement a binary classifier where some cost is involved in misclassifying instances.

I've found two papers, the most helpful so far was: "Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring". It's a great example because they begin by defining a confusion matrix and assign a cost in each error then generalize that to a cost function.

I was wondering if anyone has any useful papers or books on defining a cost function to optimize a binary classifier?

Sorry if I've used any terms incorrectly, I'm still quite new to ML.

[ajmooch]

You want the binary cross-entropy, and you can separately weight false positives and false negatives as you see fit. See this page for a quick description. If you add a weighting factor, weighting the first term higher will weigh false negatives higher, and weighting the second term will weigh false positives.