3.1 Introduction

There are multiple Machine Learning problems that can be solved using Supervised learning Machine Learning techniques. These problems require predicting an output variable (objective field) given a number of input variables (input fields). They can be divided into Classification and Regression depending on whether you need to predict a category (label or class) or a continuous value (a real number), respectively. To learn more about concrete use cases for both problems refer to section 1.1 .

Linear Regression is a supervised Machine Learning technique that can be used to solve regression problems. These problems can also be solved with other Machine Learning methods, such as models, ensembles, or deepnets. These methods are explained in Chapter 1 , Chapter 2 , and Chapter 5 respectively. Depending on the problem you are trying to solve and the data available, some techniques may perform significantly better than others. The main difference between linear regression and others is that linear regression assumes your objective field has a linear relationship with your input fields. For this reason, linear regressions work best in those problems where this assumption is accurate.

This chapter contains a comprehensive description of BigML’s linear regression models including how they can be created with 1-click (section 3.3 ), all configuration options available (section 3.4 ), and the different visualizations provided by BigML (section 3.5 ). See section 3.6 for an explanation of how linear regressions can be used to make predictions. You can also export your linear regressions in different formats to make local predictions faster at no cost (Local Predictions ). The process to evaluate your linear regressions’ predictive performance in BigML is explained in a different chapter (Chapter 7 ).

In BigML, the third tab (Supervised) of the main menu of the Dashboard allows you to list all of your available linear regressions. The linear regression list view (Figure 3.1 ), details the Dataset used to create it, the Name, Objective (Objective Field field name), Age (time elapsed since it was created), Size, and number of evaluations, predictions, and batch predictions that have been created using that linear regression. The search menu option in the top right corner of the linear regression list view allows you to search your linear regressions by name.

By default, when you first create an account at BigML, or every time that you start a new project, your list of linear regressions will be empty. (Figure 3.2 )

Finally, in Figure 3.3 you can see the icon used to represent a linear regression.