Why are boosted trees difficult to interpret? I might not fully understand the topic but: Bootstrapped/bagged trees are difficult to interpret because the decisions are made from averaging the prediction of possibly hundreds of trees (ensemble).
But with boosted trees, don't we end up with one tree?
If so, visualizing this tree should be as simple as visualizing a normal decision tree, right?
The only difference would be that it's more unclear how it got to those specific rules since it uses boosting.
However, I've also read that it's difficult to interpret a boosted tree. Could someone please clarify?
 A: In short, the final ensemble is not a tree.
Consider this part of the Wikipedia article.
The $F_m(x)$ when $m=1$ is essentially a single tree $h_1(x)$ with its output multiplied by a learned number $\gamma_m$.
In the next iteration, you build $F_2(x)$ by using the previously created $F_1(x)$ and another initial tree $h_2(x)$.
This operation is repeated $m$ times, and then, for example if there were $m=100$ initial trees, you get your final ensemble, $F_{100}(x)=F_{99}(x)+F_{98}(x)+...+F_{1}(x) + \gamma_{100}h_{100}(x)$.
When you visualize a tree, you see the decision nodes. The output of the initial tree follows from its nodes.
However, the output of the final ensemble follows from a hundred of trees giving their outputs, then multiplying these outputs by the learned values and adding it all together.
Therefore, the final ensemble is not a tree, so you cannot visualize it as a tree. A tree is a decision graph, when a learned ensemble is a linear combination of what many trees decide to yield for a given input.
(Thanks to usεr11852 for clarifying my error. The comment section is about the previous version of the post before the fix.)
