Random forests

Random forest is a type of classifier which is the combination of multiple classifiers. It works by ensemble learning procedure, and multiple learning mechanisms are used for solving a particular problem. Here, in this method a number of assumptions are constructed and by combining them the problem is solved. Let us consider θ_m is a random vector and free from earlier vectors. The classifier h(y,θ_m) is generated by completing the training of the data. y is the input data vector in the classifier. After generating a large amount of tree, the voting for most accepted class occurs to get the classification result. The overall structure of this proposed classifier is called random forest, where a group of tree-like another {h(y,θ_m), m = 1, …} is designed for the classification purpose (Mohapatra & Mohanty, 2020). The structure of the random forest model is presented in Figure 13.11.