What are random forests and extremely random forests?

A random forest is an instance of ensemble learning where individual models are constructed using decision trees. This ensemble of decision trees is then used to predict the output value. We use a random subset of training data to construct each decision tree.

This will ensure diversity among various decision trees. In the first section, we discussed that one of the most important attributes when building good ensemble learning models is that we ensure that there is diversity among individual models.

One of the advantages of random forests is that they do not overfit. Overfitting is a frequent problem in machine learning. Overfitting is more likely with nonparametric and nonlinear models that have more flexibility when learning a target function. By constructing a diverse set of decision trees using various random subsets, we ensure that the model does not overfit the training data. During the construction of the...