20 Questions on Decision Trees 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 interviews. The document provides a comprehensive guide to Decision Trees, a popular machine-learning algorithm, covering its definition, components, working mechanism, advantages, disadvantages, and related concepts like overfitting, pruning, and Random Forest.

The document helps freshers build a strong foundation in Decision Trees, enabling them to understand and apply the algorithm effectively. It also aids experienced professionals in refreshing their knowledge and preparing for detailed technical discussions in interviews. The series’s focus on core concepts and practical applications makes it a useful tool for anyone interested in mastering Decision Trees and excelling in data science interviews.

The document provides a detailed guide to “Decision Tree” for data science and AI interviews. It covers 20 key questions on Decision Trees, explaining the algorithm’s components, working principle, and its application in classification and regression tasks. The document also discusses related concepts like Gini Impurity, Entropy, overfitting, pruning, and Random Forest, providing a comprehensive understanding of Decision Trees and their use in machine learning.

𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐜𝐨𝐯𝐞𝐫𝐞𝐝:
1.   What is a Decision Tree?

2.   What are the components of a Decision Tree?

3.   How does a Decision Tree algorithm work?

4.   What are Gini Impurity and Entropy?

5.   How is the best split determined in a Decision Tree?

6.   What is Overfitting in Decision Trees?

7.   How can you prevent overfitting in Decision Trees?

8.   What are the advantages of using Decision Trees?

9.   What are the disadvantages of Decision Trees?

10.   What is Pruning in Decision Trees?

11.   What is the role of Maximum Depth in Decision Trees?

12.   How do Decision Trees handle missing values?

13.   What is Feature Importance in Decision Trees?

14.   What is the difference between Classification and Regression Trees?

15.   What is a Random Forest, and how does it relate to Decision Trees?

16.   When should you use Decision Trees?

17.   Can Decision Trees handle categorical features?

18.   How do Decision Trees compare to Logistic Regression?

19.   What is CART?

20.   Can Decision Trees be used for Time Series Data?

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