Random Forest Algorithm

The algorithm described here can be applied to both classification and regression problems.

We will grow of forest of say $B$ trees.

1. For $b=1:B$
• Draw a bootstrap sample from the training data organized in our $\boldsymbol{X}$ matrix.
• We grow then a random forest tree $T_b$ based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached
1. we select $m \le p$ variables at random from the $p$ predictors/features
2. pick the best split point among the $m$ features using for example the CART algorithm and create a new node
3. split the node into daughter nodes
2. Output then the ensemble of trees $\{T_b\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem.

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