{"id":20019,"date":"2024-04-10T12:16:00","date_gmt":"2024-04-10T06:46:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/understanding-of-lasso-regression\/"},"modified":"2024-10-15T00:11:09","modified_gmt":"2024-10-14T18:41:09","slug":"understanding-of-lasso-regression","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/understanding-of-lasso-regression\/","title":{"rendered":"A Complete understanding of LASSO Regression"},"content":{"rendered":"\n

Contributed by: Dinesh Kumar<\/a> <\/strong><\/p>\n\n\n\n

We hope you've had a chance to go through our previous articles on regression<\/a> and ridge regression.<\/a> Building on that foundation, it's now time to explore lasso regression. <\/p>\n\n\n\n

Like its predecessors, lasso regression is a powerful tool for predictive modeling<\/a>, but it comes with its unique twist\u2014penalizing the absolute size of the regression coefficients.<\/p>\n\n\n\n

Understanding all three methods together not only deepens your knowledge of statistical techniques but also equips you with a versatile toolkit for tackling real-world data challenges.<\/p>\n\n\n\n

This article will delve into why lasso regression is essential and how it complements the techniques we\u2019ve previously discussed. If you're looking to enhance your skills further, enrolling in a machine learning course<\/a><\/strong> can provide you with the structured learning needed to master techniques like lasso regression and many others. So, let\u2019s dive in and connect these dots to see why each method is crucial and why continuing this learning journey through our blog is invaluable.<\/p>\n\n\n\n

Introduction to LASSO Regression<\/strong><\/h2>\n\n\n\n

LASSO regression, also known as L1 regularization, is a popular technique used in statistical modeling and machine learning to estimate the relationships between variables and make predictions. LASSO stands for Least Absolute Shrinkage and Selection Operator.<\/p>\n\n\n\n

The primary goal of LASSO regression is to find a balance between model simplicity and accuracy. It achieves this by adding a penalty term to the traditional linear regression <\/a>model, which encourages sparse solutions where some coefficients are forced to be exactly zero. <\/p>\n\n\n\n

This feature makes LASSO particularly useful for feature selection, as it can automatically identify and discard irrelevant or redundant variables.<\/p>\n\n\n\n

What is Lasso Regression?<\/strong><\/h2>\n\n\n\n

Lasso regression is a regularization technique. It is used over regression methods for a more accurate prediction. This model uses shrinkage. Shrinkage is where data values are shrunk towards a central point as the mean. <\/p>\n\n\n\n

The lasso procedure encourages simple, sparse models (i.e. models with fewer parameters). This particular type of regression is well-suited for models showing high levels of multicollinearity or when you want to automate certain parts of model selection, like variable selection\/parameter elimination.<\/p>\n\n\n\n

Lasso Regression uses L1 regularization technique (will be discussed later in this article). It is used when we have more features because it automatically performs feature selection.<\/p>\n\n\n\n

Step By Step Explanation Of How LASSO Regression Works<\/strong>?<\/h3>\n\n\n\n

This indicates the root mean squared error of the Lasso Regression model on predicting house prices in the test set.<\/p>\n\n\n\n

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  1. Linear Regression Model<\/strong>
    LASSO regression starts with the standard linear regression model, which assumes a linear relationship between the independent variables (features) and the dependent variable (target). The linear regression equation can be represented as follows:
    = \u03b2\u2080 + \u03b2\u2081x\u2081 + \u03b2\u2082x\u2082 + ... + \u03b2\u209ax\u209a + \u03b5<\/strong><\/li>\n<\/ol>\n\n\n\n

     Where:<\/p>\n\n\n\n