While reading about the tuning of the randomForest model here-
“If you have built a decision tree before, you can appreciate the importance of minimum sample leaf size. Leaf is the end node of a decision tree. A smaller leaf makes the model more prone to capturing noise in train data. Generally I prefer a minimum leaf size of more than 50. However, you should try multiple leaf sizes to find the most optimum for your use case.”
What does it mean to have a small leaf size? Is the leaf size the number of levels in the tree or the number of leaves at the end of the tree? And I am even not getting what leaf size is better. Larger or smaller? And why?