15 Questions on Overfitting and Underfitting 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 and techniques in data science and artificial intelligence, with a focus on questions commonly asked in interviews. The series covers a wide range of topics, from basic machine learning concepts to advanced deep learning techniques, and offers clear and concise explanations, along with practical examples and tips.

The document focuses on overfitting and underfitting in machine learning, two critical concepts that affect model performance. Overfitting occurs when a model learns the training data too well, including noise and irrelevant details, while underfitting occurs when a model is too simple to capture the underlying patterns in the data. Both overfitting and underfitting can lead to poor model performance. The document explores the differences between overfitting and underfitting, how to identify them, and techniques to prevent them. It also covers related concepts such as the bias-variance tradeoff, regularization, cross-validation, early stopping, dropout, and data augmentation. Understanding these concepts is crucial for building robust and accurate machine learning models.

𝐋𝐢𝐬𝐭 𝐨𝐟 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭:

1.   What is overfitting in machine learning?

2.   What is underfitting in machine learning?

3.   What are the main differences between overfitting and underfitting?

4.   How can we identify overfitting in a model?

5.   How can we identify underfitting in a model?

6.   What techniques can be used to reduce overfitting?

7.   What techniques can be used to reduce underfitting?

8.   What is the bias-variance tradeoff, and how is it related to overfitting and underfitting?

9.   How does Random Forest differ from a single Decision Tree?

10.   Why is cross-validation important in avoiding overfitting?

11.   How does increasing the dataset size help avoid overfitting?

12.   What role does feature selection play in avoiding underfitting or overfitting?

13.   Can early stopping prevent overfitting? How?

14.   How does dropout work in neural networks to avoid overfitting?

15.   Can data augmentation help reduce overfitting? How?

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