Beginner needing help interpreting these articles
TL/DR: Understanding MIN+GB simulation protocol, explicit feature attribution
My assignment was to see if I can incorporate any of the methods used in this article, into this project.
The first article: resulted that using a Gradient boosting decision tree algorithm in conjunction with the MIN + GB simulation protocol achieved the best performance.
What does a MIN + GB simulation protocol mean?
Second article: Uses a gradient boosting machine learning model with this explicit feature attribution can predict binding affinity with high accuracy.
What does explicit feature attribution mean?
Thank you if you can help me, anything information would be great.
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TL;DR you mean ("Too Long; Didn't Read"). Your TLDR is the same as your title, so it's no use to someone that really wants a TLDR. A more appropriate TLDR would be "Understanding MIN+GB simulation protocol, explicit feature attribution"
fixed, thanks