{"id":8054,"date":"2021-01-11T03:55:59","date_gmt":"2021-01-11T03:55:59","guid":{"rendered":"https:\/\/wealthrevelation.com\/data-science\/2021\/01\/11\/five-key-assumptions-of-linear-regression-algorithm\/"},"modified":"2021-01-11T03:55:59","modified_gmt":"2021-01-11T03:55:59","slug":"five-key-assumptions-of-linear-regression-algorithm","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2021\/01\/11\/five-key-assumptions-of-linear-regression-algorithm\/","title":{"rendered":"Five Key Assumptions of Linear Regression Algorithm"},"content":{"rendered":"
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\u00a0 Nearly 80%<\/strong> of the people build linear regression models<\/strong><\/a> without checking the basic assumptions of linear regression.<\/p>\n

Just hold for a second and think. How many times have you built linear regression models without checking the linear regression<\/strong><\/a> assumptions?<\/p>\n

If you are not aware about the linear regression algorithm. It is a famous s<\/strong>upervised machine learning algorithm<\/strong><\/a> that represents the linear relationship between a dependent<\/strong> variable and independent<\/strong> variables.<\/p>\n

It is easy to understand and implement. However, just writing a few lines of code won\u2019t work as expected.<\/p>\n

Because before implementing the linear regression, we have to take care of certain assumptions made by linear regression.\u00a0<\/p>\n<\/div>\n

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Learn the 5 key linear regression assumptions, we need to consider before building the regression model. #datascience #machinelearning #ai #regression #python<\/p>\n<\/p><\/div>\n

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It is important to understand these assumptions to improve the regression model\u2019s performance<\/strong><\/a>.\u00a0<\/p>\n

So In this article, we are going to discuss these assumptions in-depth and ways to fix them if violated<\/strong>. After gaining proper knowledge of linear regression assumptions, you can bring excessive improvement in regression models.\u00a0<\/p>\n

Before we dive further, let\u2019s look at the topic you are going to learn in this article.<\/p>\n<\/div>\n

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Linear Regression Algorithm<\/h2>\n

Before explaining the algorithm, let\u2019s see what regression is. <\/p>\n<\/div>\n

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Regression is a method used to determine the degree of relationship between a dependent variable(y) and one or more independent variables (x).\u00a0<\/p>\n

Linear regression determines the relationship between one or more independent variable (s) and one target variable.\u00a0<\/p>\n

In machine learning<\/strong><\/a>, linear regression is a commonly used supervised machine learning algorithm for regression kind of problems<\/strong><\/a>. It is easy to implement and understand.\u00a0<\/p>\n

Supervised means that the algorithm can make predictions based on the labeled data feed to the algorithm.<\/p>\n

\u00a0Mathematically, linear regression can be represented as<\/p>\n

Y = mx+c<\/strong><\/p>\n

Here,<\/p>\n