I am relatively new to machine learning and I am working on a multi-class classification problem. I am attempting to utilize OVR logistic regression. When you run through an OVR model, the end result is a vector of standardized probabilities for each class that add up to 1. If I had 4 classes, my output might look like $[0.4, 0.2, 0.3, 0.1].$

I would like to obtain the raw probabilities for each class instead of the standardized probabilities. So I want what Binary Classifier 1 gives as the probability the observation belongs to class 1, what Binary Classifier 2 gives as the probability the observation belongs to class 2, etc.

If it helps, I want these probabilities because I am trying to introduce an intermediate step into my model where I create precision thresholds for each individual binary classifier. So for Binary Classifier 1 I want to line up the observations by their raw probability and find a threshold where the model is 80% precise and only make predictions on that subset of observations.

Any help is appreciated, thank you!


I can't speak to specific details about a package that does this, but you could simply run a logistic regression for each class. For each model you would code one class as positive and the rest as negative. In your example you would train 4 models.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.