We use one-class classificationone-class classification is used when we have only "positive" labels (although some argue for using it when the quality of the data about the labels is poor) for outlier, or anomaly, detection.
With such data you cannot assess accuracy of the predictions. Technically you can check if it properly labeled all your data as "positive", but then you would conclude that the useless model that always returns "positive" label no matter of data, has perfect fit.
To judge performance of such classifier you would need to have data with "negative" labels. One thing you could do is to simulate data with artificially introduced anomalies (this is often done, e.g. in image classification where you add noise to the data, or transform the images), or simulate such data that you know that should be classified as anomaly, and use such data for testing.
The story is different if you have data about "positive" and "negative" classes, since then you can use exactly the same tools for evaluating your model as for classification in general, but then, why would you use one-class classification algorithms?
