Random Forest

Random Forest:  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. 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. 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 all the features in the input dataset. Instead, it chooses the best split

among the random subset of the features that are under consideration. Adding this

randomness tends to increase the bias of the random forest, but the variance decreases

because of averaging. Hence, we end up with a robust model.