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[D] How much of an effect, if any, does batch size have when doing hyperparameter optimization?

I have been using sci-kit optimize to do hyperparameter search (using gp_minimize specifically) for a neural network. I am working on a binary classification problem with a significant class imbalance. I have been using a batch size of 10, but just came across a tweet and notebook by Francois Chollet where he recommended using a high batch size in class imbalance problems in order so that each batch contains at least a few positive examples.

My question is can I just take the networks with the best network architectures I found via my hyperparameter search where I used a batch size of 32, but just retrain them using the same hyperparameters but using a higher batch size?

Or, would batch size have a significant effect on hyperparameter optimization, and I would be better off just redoing hyperparameter optimization but this time with a larger batch size?

Going off of that, any recommendations for how to select batch size? My data contains between 400,000 – 500,000 samples, and I’m feeding in 7 features to the network.

On a similar not of dealing with class imbalance problems – my sample data is weighted to begin with (I am working with a physics problem and the weights for each sample is the probability that that sample will occur), but I was thinking about increasing the weights of the positive data points to maybe help minimize the effect of class imbalance. Thoughts on this?

I hope my question(s) makes sense, thanks for any help!

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.