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In my CNN, I have 200 'negative' images and 50 'positive' images in my test set and I want to make a confusion matrix. My doubt is if I have to equalize the samples in the dataset because if I keep this 200 - 50 my precision falls because I have a lot of 'false positives'.

So, I have to divide the percentage of negatives by 50, or keep 200/50?

This is my results without balance the samples:

                    predicted positive         predicted negative
actual pos.              41                             9
actual neg.              31                            169

recall = 41 / 50 = 82%
precision = 41 / 72 = 57%
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I don't think there is any reason to modify the matrix so keep it as it is. Even if you scale it what purpose does it serve? At the end of the day your model does not change even if you modify your confusion matrix.

In my opinion you can use other metrics e.g. f1-score (or f beta score), AUC score, etc to judge your model. Confusion matrix only provides visualization where your model "confused" and I would say it is less useful for binary classification (as you only have False positive or False negative). Metrics above serve as better judge for evaluating your model.

This is a related question which you can probably check.

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  • $\begingroup$ Because I need to present this results in my university $\endgroup$ – Gilberto Nov 21 at 16:37
  • $\begingroup$ I want to know what is the best approach. If is equalize or not $\endgroup$ – Gilberto Nov 21 at 16:38
  • $\begingroup$ I edited my answer and provided a link. You can check it. It is quite related. Basically AFAIK, there is no method that normalize everything, without reducing information. Especially since your dataset is small, imo there is no need to simplify the confusion matrix. You can include other metric and since your model has many false positive you need to include recall/f1-score on your report $\endgroup$ – Yohanes Alfredo Nov 21 at 16:50

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