Like decision trees, Random Forests allow us to make

binary splits in our data that creates segmentations or sub groups.

By applying a series of simple rules or criteria over and

over again, which choose variables that best predict our target variable.

While decision trees proceed by searching for a split on every variable in

every node, Random Forests searches for a split on only one variable in a node.

The variable that has the largest association with the Target among

candidate explanatory variables but only among those explanatory

variables that have been randomly selected to be tested for that node.

That is, First,

a small subset of explanatory variables is selected at random.

Next the node is split with the BEST variable

among the small number of randomly selected variables.

Not the best variable of all the variables,

as is true when we are interested in creating only single decision tree.

Once the best variable from the eligible random subset of variables

is used to split the node in question.

A new list of eligible explanatory variables is selected on

random to split on the next node.

This continues until the tree is fully grown, and

Ideally there is one observation in each terminal mode.

Uniquely explained by all of the decisions that came before it.

With a large number of explanatory variables,

the Eligible variables set will be quite different from node to node.

However, Important variables will eventually make it into the tree.

And Their relative success in predicting the target variable

will begin to get them larger and larger numbers of "votes" in their favor.

The growing of each tree in a random

forest is not only based on subsets of explanatory variables at each node.

But also based on A random subset of the sample for each tree in the forest.

This process of selecting a random sample of observations is known as Bagging.

Importantly, each tree is growing on a different randomly selected

sample of Bagged data with the remaining Out of Bag data

available to test the accuracy of each tree.

For each tree, the Bagging Process selects about 60% of the original sample,

while the resulting tree is tested against the remaining 40% of the sample.

Thus, the randomly selected bag data and out of bag data,

will be a different 60% and 40% of observations for each tree.

Finally, before we start to grow our first random forest,

I want to mention the most important thing to know when interrupting the results of

random forests is that the trees generated are not themselves interpreted.

Instead, They are used to collectively rank the importance of variables in

predicting our target of interest.

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