Support Vector Regression (SVR) -- One of the Most Flexible Yet Robust Prediction Algorithms

Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems. This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. In addition to giving you an understanding of how ML algorithms work, it also provides you with Python examples to build your own ML models. While you may not be familiar with SVR, chances are you have previously heard about Support Vector Machines (SVM). SVMs are most frequently used for solving classification problems, which fall under the supervised machine learning category. These use cases utilize the same idea behind support vectors, but each has a slightly different implementation.