Intuitively understand ROC and implement it in R and Python

The field of machine learning can broadly be categorised into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses previous examples with known outputs to determine an appropriate mathematical function to solve a classification or a regression problem. This post focusses on ROC (Receiver Operating Characteristics) curve that is widely used in the machine learning community to assess the performance of a classification algorithm. This post will help you intuitively understand what an ROC curve is and help you implement it in both R and Python. This article is divided into four parts, each dealing with an objective stated above.