I'm new to machine learning and I want to use random forest for the problem I have. What I have done so far is I did the 80/20 split of the original data set.
I need to understand what will happen next when building the random forest model. I understand that the next step is taking a random sample (with replacement) from the 80% portion used for training, and use this bootstrapped data set to build a decision tree. Let's assume I have 5 features/columns, and to select a root node, a random subset of the 5 features are selected, and the variable that has the highest
Gini index is the root.Let us assume feature 2 is the root.
Next, a random subset of the remaining 4 features are selected to create child nodes for the root node. Let's assume feature 1 and 5 are selected.
My questions are:
1- after obtaining
Gini index for feature 1 and 5 and let's assume node 1 has the higher index value, how do I know if node 1 should be the left or right node?
2- does node 5 become the left/right node now? or do we selected a random subset of the remaining 3 features (3, 4, 5), find their
Gini index values and the right child becomes the node with the highest