Regularization | Lasso (L1) Regression | in Hindi/Urdu(2/3)
Channel: Codanics
Date: 2022-12-20

#python #datascience #machinelearning 
Hello and welcome to our tutorial on regularization and Lasso regression. In this video, we will explore the concept of regularization and how it can be used to improve the performance of linear regression models.

We will start by discussing the problem of overfitting, which occurs when a model becomes too complex and is not able to generalize well to new data. We will then introduce the concept of regularization, which involves adding a penalty term to the objective function of a model to encourage simpler models.

Next, we will dive into Lasso regression, which is a type of regularization that uses the L1 norm as the penalty term. We will discuss how Lasso regression can be used to select a subset of features, and how it can be implemented in Python using the scikit-learn library.

Finally, we will demonstrate the effectiveness of Lasso regression on a real-world dataset and compare it to regular linear regression.

By the end of this tutorial, you will have a solid understanding of regularization and Lasso regression, and how they can be used to improve the performance of linear regression models. Thank you for watching, and we hope you found this tutorial helpful.

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