I've created a handy mind map of 60+ algorithms organized by type.ĭownload it, print it and use it. Sample of the handy machine learning algorithms mind map. Next, let’s review some of the common names used to refer to a linear regression model. It is both a statistical algorithm and a machine learning algorithm. In applied machine learning we will borrow, reuse and steal algorithms from many different fields, including statistics and use them towards these ends.Īs such, linear regression was developed in the field of statistics and is studied as a model for understanding the relationship between input and output numerical variables, but has been borrowed by machine learning. Machine learning, more specifically the field of predictive modeling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explainability.

Isn’t Linear Regression from Statistics?īefore we dive into the details of linear regression, you may be asking yourself why we are looking at this algorithm. Photo by Nicolas Raymond, some rights reserved. Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the Excel Spreadsheet files for all examples. This is a gentle high-level introduction to the technique to give you enough background to be able to use it effectively on your own problems. You do not need to know any statistics or linear algebra to understand linear regression. How to best prepare your data when modeling using linear regression.The representation and learning algorithms used to create a linear regression model.The many names by which linear regression is known.Why linear regression belongs to both statistics and machine learning.In this post you will discover the linear regression algorithm, how it works and how you can best use it in on your machine learning projects. Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning.

Simple linear regression equation b0