Autotuning parameters with ML classification

[ElpadoCan]

Description:

Autotuning parameters as a classification problem:

1. True positives are given by the user
2. False positives are from peak detection --> all detected peaks except for true positives that lie within the masks
3. CatBoost or Random forest to classify true/false based on features
4. Rank features and show user the ranking with their max and minimum value of the true positives

[ElpadoCan]

Point number 2 requires running analysis twice, one with the input spots coordinates from the user clicking and one without to get the 0_detected_spots file.

[github-actions]

Hi there, I'm the spotMAX bot 👋. This issue had no activity for more than 180 days. For now, we have marked it as "inactive" until there is some new activity. If this issue was not solved yet, unfortunately we haven't had the time to implement it or it requires more discussion. Note that it doesn't mean it has been ignored, but a little reminder from your side would help :D. Feel free to reach out to us here or on our forum. If you think that this issue is no longer relevant, you may close it by yourself. In any case, we apologise for the inconvenience and we thank you for your patience and contributions so far!