The random forest (RF) is a very powerful technique which is used frequently in the data science field for solving various problems across industries, as well as a silver bullet for winning competitions like Kaggle. We will cover various concepts from the basics to in depth in the next chapter; here we are restricted to the bare necessities. Random forest is an ensemble of decision trees, as we know, logistic regression has very high bias and low variance technique; on the other hand, decision trees have high variance and low bias, which makes decision trees unstable. By averaging decision trees, we will minimize the variance component the of model, which makes approximate nearest to an ideal model.
RF focuses on sampling both observations and variables of training data to develop independent decision trees and take majority voting for classification and averaging for regression problems respectively. In contrast, bagging samples only observations at random and selects all columns...
