After training the algorithm, each tree in the forest gives a classification on leftover data (OOB), and we say the tree “votes” for that class. Then finally, the forest chooses the classification having the most votes over all the trees in the forest.

For a binary dependent variable, the vote will be either **YES** or **NO** , and finally, it will count up the YES votes. This is the **Random Forest (RF) score** and the percent YES votes received is the predicted probability. In the regression case, it is the average of the dependent variable.

**For example,** suppose we fit 500 trees in a forest, and a case is out-of-bag in 200 of them:

- 160 trees vote class 1
- 40 trees vote class 2

In this case, the RF score is class1 since the probability for that case would be 0.8 which is 160/200. Similarly, it would be an average of the target variable for the regression problem.