In more detail, creating the tree involves selecting some variable X and finding the point in which to split data based on which point causes the greatest reduction in residual sum squares. 

This process is repeated with each new region created until a criterion is reached (ex. no region has more than 10 observations)

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Roughly speaking, there are two steps.
1. We divide the predictor space—that is, the set of possible values for X1, X2, . . . , Xp—into J distinct and non-overlapping regions, R1,R2,...,RJ.
2. For every observation that falls into the region Rj , we make the same prediction, which is simply the mean of the response values for the training observations in Rj.

Steps of creating a decision tree

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

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