# Tree complexity and gamma parameter in xgboost

According to xgboost paper, regularization is given by:

$$\Omega(f) = \gamma T + \lambda || w||^2$$

where $$\gamma$$ is the complexity of a tree (i.e., number of leaves in the tree).

The parameter gamma in xgboost library, on the other hand, controls the minimum split at a node in order to proceed. Hence, is the $$\gamma$$ in the equation above used by xgboost software package? I could not find any reference to it.

In the paragraph following equation (1):

$$T$$ is the number of leaves in the tree.

$$\gamma$$ is a hyperparameter that affects how much regularization occurs on the size (number of leaves) of the tree.

Now it turns out that you can interpret $$\gamma$$ (at least roughly, see note at bottom) as ([source]):

Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger gamma is, the more conservative the algorithm will be.

You can see that from equation (2), the regularized objective:

$$\mathcal{L}(\phi) = \sum_i l(\hat{y}_i, y_i) + \sum_k \Omega(f_k),\\ \text{where }\Omega(f)=\gamma T + \frac12 \lambda \|w\|^2.$$

By making the split, you increase $$T$$ by one, so the penalty increases by $$\gamma$$, and so your base loss term $$l$$ needs to decrease by at least $$\gamma$$ for this to be an overall improvement. Note: Of course, this ignores what happens to the leaf weights $$w$$ in splitting one node into two.