Final answer:
The statement is false; in a regression tree, each leaf represents a partition with a specific prediction value, not a separate regression model. The tree provides a piecewise-constant approximation of the input space.
Step-by-step explanation:
The statement that in a regression tree, every leaf of the tree has a different regression model that might use different attributes, have different coefficients, etc., is false. In a regression tree, each leaf represents a partition of the input space created by the splits at the decision nodes, and it corresponds to a specific prediction value, not a separate regression model. The tree as a whole represents a piecewise-constant approximation, where each leaf gives a constant prediction for all instances that fall within that leaf's partition.
As you progress from the root to a leaf in a regression tree, you follow a path determined by certain conditions on the features. Once you reach a leaf, the final prediction is typically the mean (or sometimes median) of the target variable for the training instances that are sorted to that leaf. It's important to note that unlike in ensemble methods like random forests or boosting, where multiple models are indeed created and combined, a single regression tree is just one model with a constant prediction for each leaf.