Ok, but in my (very long) answer, I did mention about tree-based methods and Naive Bayes:
Tree-based models and probability-based algorithms like Naive Bayes may not require scaling.
Tree-based methods don't require scaling as they are are not distance-based algorithms, the splits are done based on the order of the data and information generated by splitting at a certain threshold, but there are no influences of the individual values, ranges or distributions on the split results.
Naive Bayes is a probability-based algorithm, it calculates probabilities from the data's distribution and is invariant to the scale of the data.
Some further ressources :
https://stats.stackexchange.com/questions/244507/what-algorithms-need-feature-scaling-beside-from-sv...
https://www.dataschool.io/comparing-supervised-learning-algorithms/
https://forecastegy.com/posts/do-decision-trees-need-feature-scaling-or-normalization/
Does this complementary response answer your question ?
Victor GUILLER
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)