Timeline for How can realize the evaluation/validation of unsupervised models through unlabeled data?
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| Dec 4, 2020 at 9:07 | history | edited | Mario | CC BY-SA 4.0 |
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| Dec 3, 2020 at 15:20 | comment | added | Mario | Right, Thanks for your quick input. Can we say that DL or Clustering methods do this labeling job internally with some amount of error we try to detect anomalous or abnormalities? so What are your short answers to bullet Qs generally aside anomaly concept? What is the state-of-the-art then? I know one approach could be to use PU learning or label some part of data anyway as it is mentioned in this post | |
| Dec 3, 2020 at 15:01 | comment | added | Nuclear Hoagie | If I understand correctly, you have no true labels of what is or is not anomaly? In that case, there is no concept of loss or error, since you have absolutely no way of telling whether your output is correct or not - there's nothing to compare your predictions against. Any loss/error function requires you to know how far off the prediction is from the "correct" answer, but you can't do that without knowing the correct answer. | |
| Dec 3, 2020 at 14:50 | history | edited | Mario | CC BY-SA 4.0 |
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| Dec 3, 2020 at 14:38 | history | asked | Mario | CC BY-SA 4.0 |