10 Questions on Linear Regression in Data Science and AI Interviews

The Data Science & AI Interview Question Series is a valuable resource for freshers and experienced data science professionals looking to crack interviews. It provides a comprehensive guide to the most commonly asked questions in data science and AI interviews, covering a wide range of topics.

Today we will cover Linear Regression, which is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It is a versatile technique that can be used for both prediction and inference. There are several key assumptions that must be met in order to use linear regression appropriately. Overfitting and underfitting are common problems that can occur when using linear regression. There are several methods for minimizing the cost function in linear regression, including gradient descent and the normal equation. Regularization is a technique that can be used to prevent overfitting.

The key questions covered in this document are:

1.   What is Linear Regression?

2.   What is the difference between Simple Linear Regression and Multiple Linear Regression?

3.   What are the key assumptions of Linear Regression?

4.   What is R-squared (R2), and what does it tell us?

5.   What is multicollinearity, and how can you handle it in Linear Regression?

6.   What is the difference between Overfitting and Underfitting in Linear Regression?

7.   How do you interpret the coefficients in a Linear Regression model?

8.   What is the cost function in Linear Regression, and how is it minimized?

9.   What is the difference between Gradient Descent and Normal Equation in Linear Regression?

10.   What is Regularization in Linear Regression, and why is it important?

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