A random forest is a supervised machine learning algorithm based on ensemble learning. It is used for both regression and classification problems. The general idea behind random forests is to build multiple decision trees and aggregate them to get an accurate result. A decision tree is a deterministic algorithm, which means if the same data is given to it, the same tree will be produced each time. They have a tendency to overfit, because they build the best tree possible with the given data, but may fail to generalize when unseen data is provided. All the decision trees that make up a random forest are different because we build each tree on a different random subset of our data. A random forest tends to be more accurate than a single decision tree because it minimizes overfitting.
The following diagram demonstrates bootstrap sampling being done...
