Regression Model : Understanding Regression Model and Why Should I Use It ?





Introduction :


Basic understanding of regression model is a statistical model that is used to predict a continuous outcome variable. The outcome variable is often referred to "dependent variable" while the predictor variables are referred to "independent variables."


The goal of a regression model is to find the best fit line or curve that describes the relationship between the dependent variable and the independent variables. The best fit line or curve is the line or curve that minimizes the sum of the squared errors.


There are many different types of regression models, but the most common type is the linear regression model. The linear regression model is a linear model, meaning that the outcome variable is a linear function of the predictor variables. For example, you might want to predict the price of a house based on its size, or the length of time a patient will stay in the hospital based on their age.


The linear regression model is the simplest type of regression model, but it is also the most restrictive. The linear regression model makes a number of assumptions about the data, including that the outcome variable is a linear function of the predictor variables, that the predictor variables are independent of each other, and that the errors are normally distributed.


If the data do not meet these assumptions, then the linear regression model will not be a good fit for the data. In these cases, a different type of regression model may be more appropriate.


There are a few different types of regression models, including:

  • Linear Regression

  • Ridge Regression

  • Lasso Regression


 

Linear Regression


Linear regression is a statistical method used to predict a dependent variable using one or more independent variables. The method is used when the dependent variable is continuous and the independent variables are either continuous or categorical.



Image by Dan White 1000 on Shutterstock


 

Lasso Regression


Lasso regression is a type of linear regression that uses shrinkage. Shrinkage is a process where data values are shrink towards a central point, such as the mean. Lasso regression shrinks the coefficients of less important features to exactly 0. This results in a model that is simpler and easier to interpret. It uses L1 regularization technique. L1 regularization is a type of regularization that encourages sparsity in the model or the presence of fewer parameters. This is done by adding a penalty on the absolute value of the coefficients of the model.


 

Ridge Regression


Ridge regression is a type of linear regression which is used to predict continuous values. It uses L2 regularization technique. L2 regularization adds a penalty equivalent to square of the magnitude of the coefficients. Ridge regression performs better than linear regression when there are a lot of features in the data. When the number of features is less, ridge regression performs similarly to linear regression.



Image by David Santubo on TowardsDataScience


 

Conclusion :


In this post, you have learned about different types of regression and the usefulness of them. Apart from these mentioned regression models, there are other types of regression in machine learning, including Elastic Net Regression, JackKnife Regression, Stepwise Regression, and Ecological Regression.


All of these different types of regression models can be used to build a model depending on what kind of data is available to maximize the accuracy. You can start exploring and building regression model without coding by using our No-Code Ever AI platform (https://ever-ai.app/)


 

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