‘Random’ in Random Forest refers to mainly two processes –

- Random observations to grow each tree.
- Random variables selected for splitting at each node.

**Random Record Selection:** Each tree in the forest is trained on roughly 2/3rd of the total training data (exactly 63.2%) and here the data points are drawn at random with replacement from the original training dataset. This sample will act as the training set for growing the tree.

**Random Variable Selection:** Some independent variables(predictors) say, m are selected at random out of all the predictor variables, and the best split on this m is used to split the node.

**NOTE:**

- By default, m is taken as the square root of the total number of predictors for classification whereas m is the total number of all predictors divided by 3 for regression problems.
- The value of m remains constant during the algorithm run i.e, forest growing.