15 Questions on Logistic 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 preparing for job interviews. It provides a comprehensive overview of key concepts, algorithms, and techniques commonly tested in data science interviews.

The series covers a wide range of topics, from basic statistics to advanced machine learning, presented in a clear and concise manner with easy-to-understand explanations and practical examples. Whether you’re a new graduate or a seasoned data scientist, this series will help you refresh your knowledge, deepen your understanding, and gain the confidence to succeed in your next interview.

This document focuses on Logistic Regression, a fundamental machine learning algorithm used for binary classification problems. It explains how the algorithm works, its assumptions, evaluation metrics, and limitations. It also covers advanced concepts like regularization, multi-class classification, and handling missing values. The document provides clear explanations, practical examples, and mathematical details to help readers fully grasp the concepts behind Logistic Regression and its applications in data science.

Questions Covered:
1.   What is Logistic Regression?

2.   How is Logistic Regression different from Linear Regression?

3.   What is the Sigmoid Function, and why is it used in Logistic Regression?

4.   What is the cost function in Logistic Regression? Why isn’t MSE used?

5.   What are odds and log-odds in Logistic Regression?

6.   What are some key assumptions of Logistic Regression?

7.   How is model performance evaluated for Logistic Regression?

8.   What is the purpose of the threshold in Logistic Regression?

9.   How does regularization help in Logistic Regression?

10.   How does Logistic Regression handle multi-class classification?

11.   What is the difference between the model coefficients and the odds ratios in Logistic Regression?

12.   Can Logistic Regression be used for regression tasks?

13.   How do we interpret the coefficients in Logistic Regression?

14.   What are some limitations of Logistic Regression?

15.   How do you handle missing values in Logistic Regression?


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