Comments (2)
The main reason may be that the simulated data created by sklearn.make_classification is too simple. The default Logistic Regression classifier can easily fit. And there may be a lot of samples that is similar. It would be useless to add the similar samples after the one has been learned by the model.
And the results of a single experiment may be random. The results are more convincing if the number of experiments(round) is set as a large value.
There is no guarantee that the QBC is must better than Random in any case. The QBC is generally better.
from alipy.
Thanks for the quick response, which generally addressed my issue.
from alipy.
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