For questions related to the cross-validation techniques (e.g. k-fold cross-validation or leave-one-out cross-validation) used in machine learning to assess the quality (e.g. average accuracy) of the models.
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What are "development test sets" used for?Should I continue training if the neural network attains 100% training accuracy?
What is the best measure for detecting overfitting?
Is k-fold cross-validation more effective than splitting the dataset into training and test datasets to prevent overfitting?
Why is the validation loss less than the training loss, and what can be said about the effect of the learning rate?
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