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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
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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)....
0
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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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