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I have been trying to set up a ConvNet to classify some data. This data should be classified to either 1 (being what I need to get from the image) and 0 for everything that is irrelevant. I have successfully extracted 50k samples (positive) but I am having a hard time of getting negative samples. What would happen if I trained my net with 15k positive samples and lets say 5k negative ? I have read that this could be a problem for statistical algorithms ... is that also relevant to convolutional neural networks ?

Andy
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Engin007
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    Possible duplicate of [Classification/evaluation metrics for highly imbalanced data](https://stats.stackexchange.com/questions/222558/classification-evaluation-metrics-for-highly-imbalanced-data) or https://stats.stackexchange.com/questions/283170/when-is-unbalanced-data-really-a-problem-in-machine-learning or https://stats.stackexchange.com/questions/247871/what-is-the-root-cause-of-the-class-imbalance-problem – Sycorax Aug 15 '18 at 23:25

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