1. What is a Decision Tree in Machine Learning?
A Decision Tree is like a flowchart that helps the machine make decisions step by step. It asks questions about the data at each step and leads to a final result. It is used for both classification (Yes/No, Spam/Not Spam) and regression (numerical predictions).
2. Why is it called a “Tree”?
Because it has:
- A root at the top
- Branches spreading out
- Leaves at the end
Just like a real tree — but upside down!
3. How does a Decision Tree learn?
It looks for the best question to split the data at every step. The goal is to split the data in such a way that similar items end up together.
4. What is a Node?
A node is a point where the data is checked or a decision is made.
- The first node is the Root Node
- Middle nodes are Decision Nodes
- Final nodes are Leaf Nodes
5. What is Gini Impurity in simple words?
Gini Impurity tells how “mixed” a node is.
- If a node has mixed classes → high impurity
- If a node has only one class → low impurity
The model tries to reduce impurity with every split.
6. What is Entropy?
Entropy is another measure of impurity (like disorder). Higher entropy means the data is more scattered or less pure.
7. What is Information Gain?
Information Gain tells how much better a node becomes after a split.
Higher the Information Gain → better the split.
8. What is Overfitting in Decision Trees?
Overfitting means the tree learns too many details from the training data, making it bad at predicting new data. Think of it as “learning the book, not the subject.”
9. How do we avoid Overfitting?
- Limit tree depth
- Prune the tree
- Set minimum samples per split
- Use Random Forest instead of one tree
10. What is Pruning?
Pruning means cutting off unnecessary parts of the tree to make it simpler and better at generalizing new data.
11. Difference between Pre-Pruning and Post-Pruning?
- Pre-Pruning: Stop tree growth early
- Post-Pruning: Allow full growth and then remove useless branches
12. Can a Decision Tree handle missing values?
Yes. Decision Trees can handle missing values by using alternative branches or splitting based on existing available data.
13. What type of data can Decision Trees work with?
Decision Trees can handle:
- Numerical data
- Categorical data
- Mixed data
No need for normalization or scaling.
14. Advantages of Decision Trees
- Very easy to understand
- Visual and interpretable
- Works well with small datasets
- No need for complex math
15. Disadvantages of Decision Trees
- Can become very large and confusing
- Can overfit easily
- Sensitive to small changes in data
16. What is a Random Forest in simple words?
Random Forest is a bunch of Decision Trees working together. Each tree votes, and the majority decides the answer. This reduces overfitting and improves accuracy.
17. Where are Decision Trees used in real life?
- Loan approval
- Disease diagnosis
- Email spam detection
- Customer churn prediction
- Fraud detection
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