# How to interpret fit from regression (decision) tree which has used 0 variables

I have fit a regression tree to my dataset and the output from summary(tree1) is as follows:

Regression tree:
tree(formula = y ~ X)
Variables actually used in tree construction:
character(0)
Number of terminal nodes:  1
Residual mean deviance:  0.985 = 71680 / 72770
Distribution of residuals:
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-1.0050 -1.0050 -0.3122  0.0000  0.7842  2.2140


So seemingly the tree has found no variables/splits worth including. The reason this is curious is because other methods have found trends in the data. For example a linear regression line found several significant variables and p-value for F-statistic was ~0 (although this is a very noisy dataset based on human behaviour and R-squared was not much bigger than 0).

How can I interpret this regression tree output? There are clearly trends in the dataset as shown by linear regression. Are regression trees more focused on accurate predictions and so won't fit well to a noisy dataset?