Predicting negative class rather than target in SVM

Question

I am trying to classify a target group from controls using a SVM. I am predicting probabilities, and noticed that when predicting the target class, the SVM performance was horrible (AUC ~0.2). This made me think, if I predicted the control class, then shouldn't the performance improve - which it did.

Essentially, I have an algorithm that accurately predicts the opposite of what I want.

Is there anything inherently wrong with this, and would it be ok to use the probability that the target group are controls as a way of classifying the target group?

Additionally, does anyone have any idea why the performance is poor predicting the targets, but good at predicting controls?

welcome to the forum. IIUC.can you try inverting labels? map 0 to 1 and 1 to 0. I had similar problem and later inverted the labels (and it did show some improvement). not sure whether this really addresses your problem — The Great, Feb 24 '22 at 06:58
Thank you for the welcome! Just tried that and it has worked, but that seems quite strange - do you have any idea why that is the case? — jmero, Feb 24 '22 at 07:08
I just asked this question few days ago. you can refer this post - https://stats.stackexchange.com/questions/563545/why-metrics-focus-on-maximizing-only-majority-class-in-binary-classification — The Great, Feb 24 '22 at 07:40
Does this answer your question? Why metrics focus on maximizing only majority class in binary classification? — Karolis Koncevičius, Feb 26 '22 at 07:42

Predicting negative class rather than target in SVM

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