A Random Forest is a particular 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 things in ensemble learning is to ensure that there's diversity among individual models.

One of the best things about Random Forests is that they do not overfit. As we know, overfitting is a problem that we encounter frequently in machine learning. 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 tree, the nodes are split successively and the best thresholds are chosen to reduce the entropy at each level. This split doesn't consider...