Algorithm 2: Random Forest algorithm pseudocode.

input: Number of Trees ($m$), random subset of the features ($mtry$), training dataset ($\mathit{D}$) output: random forest ($\mathit{R}\mathit{F}$) /1/ $\mathit{R}\mathit{F}$ is empty /2/ for each $i$ to $m$ do /3/ ${\mathit{D}}_i$ = Bootstrap Sample ($\mathit{D}$) /4/ $\mathit{D}{\mathit{T}}_i$ = Random Decision Tree (${\mathit{D}}_i$, $mtry$) /5/ $\mathit{R}\mathit{F}$ = $\mathit{R}\mathit{F}\cup \mathit{D}{\mathit{T}}_i$ /6/ end /7/ return $\mathit{R}\mathit{F}$