Your Guide to Linear Regression Models

Your Guide to Linear Regression Models

This article explains linear regression and how to program linear regression models in Python. In this post, I’ll focus on Linear Regression models that examine the linear relationship between a dependent variable and one (Simple Linear Regression) or more (Multiple Linear Regression) independent variables.

Interpretability is one of the biggest challenges in machine learning. A model has more interpretability than another one if its decisions are easier for a human to comprehend. Some models are so complex and are internally structured in such a way that it’s almost impossible to understand how they reached their final results. These black boxes seem to break the association between raw data and final output, since several processes happen in between.

But in the universe of machine learning algorithms, some models are more transparent than others. Decision Trees are definitely one of them, and Linear Regression models are another one. Their simplicity and straightforward approach turns them into an ideal tool to approach different problems. Let’s see how.

You can use Linear Regression models to analyze how salaries in a given place depend on features like experience, level of education, role, city they work in, and so on. Similarly, you can analyze if real estate prices depend on factors such as their areas, numbers of bedrooms, or distances to the city center.

In this post, I’ll focus on Linear Regression models that examine the linear relationship between a dependent variable *and one (Simple Linear Regression) or more (Multiple Linear Regression) *independent variables.

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Which methods should be used for solving linear regression?

As a foundational set of algorithms in any machine learning toolbox, linear regression can be solved with a variety of approaches. Here, we discuss. with with code examples, four methods and demonstrate how they should be used.ere are many different methods that we can apply to our linear regression model in order to make it more efficient. But we will discuss the most common of them here.