Algorithm 4. Extreme gradient boosting (XGBoost) algorithm.

Input: I, instance set of current node Input: d, feature dimension Gain ← 0 $G\leftarrow {\displaystyle {\sum }_{\mathrm{i}\in I}{g}_i},H\leftarrow {\displaystyle {\sum }_{\mathrm{i}\in I}{h}_i}$ for k = 1 to m do     $G_L\leftarrow 0,H_L\leftarrow 0$    for j in sorted (I, by ${\mathrm{x}}_{\mathrm{jk}}$) do       $G_L\leftarrow G_L+g_j,H_L\leftarrow H_L+h_j$       $G_R\leftarrow G-G_L,H_R\leftarrow H-H_L$       $score\leftarrow max(score,\frac{G_L^2}{H_L+\lambda }+\frac{G_R^2}{H_R+\lambda }‒\frac{G^2}{H+\lambda })$    end end Output: Split with max score