What is the difference between finding the threshold and training a binary classifier?

Asked Feb 14 '19 at 16:44

Active Feb 14 '19 at 16:50

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I have 1-D data: the output of a Cosine similarity (COS) distance between two features. The data is bounded by [-1,1].

When I just try to find the optimum threshold for class labels - I get an accuracy for the testing data of about 75%

Then I thought to maybe train a binary classifier (namely wx+b - 1 layer of linear fully connected and pass it to a sigmoid and then to binary loss function) When I do the following I get only 71% accuracy.

What do you think is the difference between the two approaches? Why is the threshold method get higher accuracy? What am I doing wrong?

edited Feb 14 '19 at 16:50

AdamO

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asked Feb 14 '19 at 16:44

eran

Very closely related possibly an outright duplicate: Classification probability threshold – Stephan Kolassa Feb 14 '19 at 17:48
I think it quite different, I will try to emphasise the difference, here I am wondering what is the difference between just searching for an optimal margin and applying a linear transformation and a Loss function – eran Feb 14 '19 at 17:54
Searching for an "optimal margin" is nothing else than using a particular (implicit) loss function. – Stephan Kolassa Feb 14 '19 at 17:54
so what in the process do you see wrong? because the results are very different – eran Feb 14 '19 at 18:10

What is the difference between finding the threshold and training a binary classifier?

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