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4 votes

Is there an argument against using the (reviewed) predictions of a model as ground truth to further train exactly this model?

Using the (unchecked) predictions of the model as training data is an approach known as "pseudo-labeling". It can help in certain situations, depending on the underlying structure of your ...
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4 votes

Is there an argument against using the (reviewed) predictions of a model as ground truth to further train exactly this model?

The answer is: It depends. What you describe is a strategy often used to save time and costs for labelling data. It is important that the data you have already labelled (the 20%) is representative of ...
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2 votes
Accepted

What is the total number of actions and rewards count

TL;DR In the DQN paper, each environment was trained for 50 million frames, grouped in fours without overlap, so there were 12.5 million state, action, reward next-state records used. The above direct ...
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0 votes

How to deal with an unbalanced dataset?

You should use another classification metric for evaluating your model. I would just look at the confusion matrix to see how the model performance on the "interesting class" (minority class)....
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0 votes

How to deal with an unbalanced dataset?

You can use a data augmentation technique like SMOTE to oversample the minority class. It will help you have a more balanced dataset. Here is a nice guide on it: https://machinelearningmastery.com/...
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1 vote

How to deal with an unbalanced dataset?

4861/5111 is about 95.1%, so it looks like your classifier is probably predicting every patient as "no stroke" (i.e. it is not really doing anything useful). The thing to do is to work out ...
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