Random forest is an extension of the decision tree model that we just discussed. In practice, Decision Trees are simple to understand, simple to interpret, fast to create using available algorithms, and overall, intuitive. However, Decision Trees are sensitive to small changes in the data, permit splits only along an axis (linear splits) and can lead to overfitting. To mitigate some of the drawbacks of decision trees, whilst still getting the benefit of their elegance, algorithms such as Random Forest create multiple decision trees and sample random features to leverage and build an aggregate model.
Random forest works on the principle of bootstrap aggregating or bagging. Bootstrap is a statistical term indicating ...
