Flukes are normal

This is not an isolated incident. For example, we could grow additional trees from random subsets of the training data.

From the training set of 250 homes…

… let's draw four random samples of 200 homes each and grow trees based on them.

The resulting trees all look reasonably different and also have single-home leaf nodes.

These seemingly-esoteric homes that may result in single-data-point leaf nodes are actually a normal part of any data set. They are an outcome of the method for fitting the model.

When the minimum node size parameter is one, the tree grows until every branch has a homogeneous leaf node.

For a given data set, growing the tree on a different set of homes changes what the branches overfit to, but overfitting still occurs. Models that overfit are unstable and sensitive to small changes in the training data and thus high variance.

The Bias-Variance Tradeoff One way to address errors from overfitting is to impose limits on how a tree grows by changing the minimum-node-size threshold. As the minimum-node-size threshold increases, there are fewer splits. The trees get less bushy. The accuracy of the each tree improves as errors due to variance decrease. As the minimum-node-size threshold continues to increase, the accuracy begins to deteriorate from error due to bias. Until you get back to a stump.

A model that is overly-simplistic is just as problematic as one that is overly-scrupulous. Errors due to bias and those due to variance are distinct. Understanding the tradeoff between bias and variance (and how different model types let you balance the two) is foundational to modeling well.