.NET - Machine Learning Through Probabilistic Programming

You can learn the weights during training and then use them directly in prediction. If you make a tiny change in your model by having the noisy score at the end thresholded against zero, the new label is suddenly one of two classes. You've just modeled a binary linear classifier, maybe without even knowing what it's called. Second, you don't need to try and adapt your problem and data to one of the existing ML algorithms. This should be obvious--you designed the model for your problem, so it fits your data.