A random forest is a set of decision trees where each tree is constructed from a sample chosen randomly from the initial data. This process is called bootstrap aggregating. Its purpose is to reduce variance and bias in the classification made by a random forest. The bias is further reduced during a construction of a decision tree by considering only a random subset of the variables for each branch of the tree.

Once a random forest is constructed, the result of the classification of a random forest is the majority vote from among all the trees in a random forest. The level of the majority also determines the amount of the confidence that the answer is correct.

Since a random forest consists of decision trees, it is good to use it for every problem where a decision tree is a good choice. Since a random forest reduces bias and variance that exist in a decision tree classifier, it outperforms a decision tree algorithm.