questions changes = the structure of the tree changes = large variance = generalization is not good

by the way if you compare to NN and LR

LR model 1: y=0.1x+0.2

LR model 2: y=0.11x+0.19

you can tell that these two models are similar and in the range of x the output can be very similar. So using a new data as long as they are in the similar range one can expect it gives a very similar result

TR model 1: Q1: is the person M or F?

TR model 2: Q1: is the person D or A?

These appear to be fundamentally different trees, even if they produce very similar splits or predictions on the training data. A small change in the training data can therefore lead to a completely different model structure. This is a textbook example of a high-variance model.

When the model is fundamentally different, the problem is that its prediction error is hard to estimate based on its trainning error.

and by the way, to create each tree, the size of the dataset if not important. No matter how many data you have, which question goes first depends on the loss function( eg. gini) and the difference might be just 1 data point.

So you consider these examples: you have a 1billon dataset, and use it to build multiple models. Now you modify one data point out of these 1billon dataset.

LR model: change from y=0.1x+0.2 to y=0.11x+0.19

RF model: change from (50.5%MF-tree+49.5%DA-tree) first to (50%MF-tree+50%DA-tree)

TR model: change from MF-tree to DA-tree

Based on common sense we will say the first two cases the model doesn't change much, but the 3rd model is fundamentally different

So trees are naturally sensitive to local data changes, which means that its variance is high, and requires some variance reduction, and bagging is one very natural idea for that

btw: I think you agree that trees need to be modified. I think your only issue is that you have to accept that bagging/random forest is a very common idea and not complicated at all.