I'm confused about the intuition behind decision trees when used to describe continuous targets in machine learning.

I understand that decision trees uses splits based on feature values to decide which branches of a tree to go down to get to a leaf value.

It intuitively make sense to me when doing inference on classification based on nominal targets because each leaf would have as specific value (label), so after going down enough branches one eventually arrives at discrete value which is the label.

But if we're doing regression where a machine learning model predicts a value on a continuum, for example a real number between 0 and 100, how could there be enough leaves to allow the model to output any real number between 0 and 100?

CodePudding user response：

Regression trees are only what you could call 'pseudo continuous' in contrast for example to linear regression models. For the 'leaves' the outputs will have a steady value for certain ranges of the independent variable(s) - dependent on the mentioned 'splits'.

However, there exists some academic work that fits (regression) models in the nodes (...). See the accepted answer here:

https://stats.stackexchange.com/questions/439756/decision-tree-that-fits-a-regression-at-leaf-nodes