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I think it is important to think about the application of that classifier and get the negative class images to be from a similar distribution as will be your application. For example if you want to classify blog images get the negative examples from blogs, if you want to classify facebook photos, get the facebook photos.
Note that this should apply also for ...
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I afraid I couldn't reproduce the same behavior on my machine.
I suggest the following:
Check for covid_data.columns - As seen in my screenshot, please verify the columns the DataFrame reads.
Provide the rest of your Jupyter notebook.
You already tried restarting your notebook, but please verify you indeed restart the iPython Kernel. please do so by ...
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In your case, you have first to deal with the biological data complexity.
I don't know the minimum sampling rate to detect brain epilepsia or any brain behavior. I would recommend to study some articles to know the best practices about EEG signal analysis like this one : https://www.frontiersin.org/articles/10.3389/fneur.2020.00375/full
Maybe there are good ...
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Since it is a multi-class classification problem, look at the confusion matrix to find the specific categories that are being misclassified. Then acquire more data for categories where the most mistakes happen.
Another approach would to examine the decision boundary and acquire more data near the decision boundary.
These techniques can be combined - request ...
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