Closed sskoldas closed 3 months ago
Hi, Seda!
Currently, GDmicro was developed for binary classification. If you want to classify multi-label of one test sample, one potential solution is to run models built for different diseases respectively.
Thank you for the quick response! I have a microbiome dataset for one disease but the disease has different clinical conditions. I want to classify these clinical conditions and find biomarkers. What do you mean by "run models built for different diseases respectively"? Are you suggesting that I run the model for each class label, i.e. label it as "one versus all"?
Hi, in your case, you can try the model for each condition (as the binary classification). For example, sample A has three disease conditions: c1 (1), c2(0), c3 (1), similarly, sample B could have: c1 (0), c2 (1), c3 (1), and so on.
Then you can build models for c1, c2, c3, respectively. As a result, there are three models: m1, m2, and m3.
Then, you can run these models for test samples: m1 -> predict: c1: 0 or 1 m2 -> predict: c2: 0 or 1 ……
Not sure whether this operation can meet your requirement. Let me know if I have a misunderstanding about your requirement or you have any other problems when using GDmicro.
Hello,
I haven't tried it yet, but I'm curious about using GDmicro for multi-class classification. Is it compatible for multi-class classification? Or was it developed only for binary classification?
Best!