Decision Tree Interview Questions & Answers for Beginners

1. What is a Decision Tree?

A Decision Tree is a supervised machine learning algorithm used for classification and regression tasks. It splits data into branches based on conditions and helps make predictions by following a path from the root node to a leaf node.

2. How does a Decision Tree work?

A Decision Tree works by repeatedly splitting the dataset based on the feature that gives the best separation of classes. Each split reduces impurity, and the process continues until a stopping condition is reached or all data is classified.

3. What are the main components of a Decision Tree?

Root Node: Starting point of the tree
Decision Node: A node where the split happens
Leaf Node: Final output or prediction
Branches: Paths connecting decision nodes

4. What is Gini Impurity?

Gini Impurity measures how often a randomly chosen element from the set would be incorrectly labeled. A lower Gini value means a purer node.

5. What is Entropy?

Entropy is a measure of impurity or randomness in the data. It is used in Information Gain to decide the best split in Decision Trees.

6. What is Information Gain?

Information Gain measures how much a feature improves purity after a split. The feature with the highest Information Gain is chosen for splitting the node.

7. Difference between Gini Impurity and Entropy?

Gini Impurity: Faster and simpler to compute
Entropy: Includes logarithmic calculations
Both measure impurity, and the choice depends on algorithm preference.

8. What is Overfitting in Decision Trees?

Overfitting happens when the tree learns too much detail from the training data, making it perform badly on new unseen data.

9. How to avoid Overfitting in Decision Trees?

Pruning
Setting maximum depth
Setting minimum samples per leaf
Limiting the number of features in splits

10. What is Pruning?

Pruning is a technique to reduce the size of a Decision Tree to prevent overfitting. It removes unnecessary branches that do not improve prediction.

11. What is the difference between Pre-Pruning and Post-Pruning?

Pre-Pruning: Stops tree growth early
Post-Pruning: Grows full tree and then removes weak nodes

12. Can Decision Trees handle missing values?

Yes, Decision Trees can handle missing values by using surrogate splits or distributing samples based on existing patterns.

13. What type of data can Decision Trees handle?

They can handle both numerical and categorical data, making them very flexible.

14. What are the advantages of Decision Trees?

Easy to understand and visualize
No need for feature scaling
Works for both classification and regression
Handles nonlinear data

15. What are the disadvantages of Decision Trees?

Prone to overfitting
Can become complex with deep trees
Small changes in data may change the tree drastically

16. What is a Random Forest?

Random Forest is an ensemble of multiple Decision Trees. Each tree makes a prediction, and the final output is based on majority vote or averaging, solving the overfitting problem.