5 votes
Accepted

What is the best measure for detecting overfitting?

tl;dr The safest method I've found is to use cross-validation for hyperparameter selection and a hold-out test set for a final evaluation. Why this isn't working for you... In your case, I suspect you'...
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  • 3,103
4 votes
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Is k-fold cross-validation more effective than splitting the dataset into training and test datasets to prevent overfitting?

K-fold cross-validation is probably preferred in terms of completeness and generalization: you ensure that the system has seen the complete dataset for training. However, in deep learning this is ...
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3 votes

Is k-fold cross-validation more effective than splitting the dataset into training and test datasets to prevent overfitting?

Purely in terms of overfitting, and assuming you train both for equal amounts of time, 70/30 is probably better but performance is not going to be very good. Not training on %30 of data will make both ...
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2 votes
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Why would the "improvement" be the result of random initialization, and so why should we use multiple runs?

Neural networks use random number generators in multiple places. Most notably for weight initialisation, but also for features such as dropout, selecting minibatches within epochs, and train/cv split ...
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