Random Forests Algorithm explained with a real-life example and some Python code

Random Forests is a Machine Learning algorithm that tackles one of the biggest problems with Decision Trees: variance. Even though Decision Trees is simple and flexible, it is greedy algorithm. It focuses on optimizing for the node split at hand, rather than taking into account how that split impacts the entire tree. A greedy approach makes Decision Trees run faster, but makes it prone overfitting. An overfit tree is highly optimized to predicting the values in the training dataset, resulting in a learning model with high-variance.