![]() Simple linear regression is a statistical model widely used in machine learning regression tasks. On the other hand, whenever you’re facing more than one feature to explain the target variable, you are likely to employ a multiple linear regression. In the first scenario, you are likely to employ a simple linear regression algorithm, which we’ll explore more later in this article. If you want to predict a house’s price only based on its squared meters, you will fall into the first situation (one feature), but if you are going to predict the price based on its squared meters, its position and the liveability of the surrounding environment, you are going to fall into the second group for multiple features. To give you an example, let’s consider the house task above. Regression tasks can be divided into two main groups: Those that only use one feature to predict the target, and those that use more than one feature for that purpose. Imagine that you want to predict the price of a house based on some relative features, the output of your model will be the price, hence, a continuous number. ![]() As a result, the algorithm will be asked to predict a continuous number rather than a class or category. Regression involves numerical, continuous values as a target. In classification, the target is a categorical value (“yes/no,” “red/blue/green,” “spam/not spam,” etc.). It differs from classification because of the nature of the target variable. ![]() Since supervised machine learning tasks are normally divided into classification and regression, we can collocate linear regression algorithms into the latter category. Linear regression is a family of algorithms employed in supervised machine learning tasks. Why is that? It’s helpful to first understand linear regression algorithms. ![]() Ordinary least squares (OLS) regression is an optimization strategy that allows you to find a straight line that’s as close as possible to your data points in a linear regression model.
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