Decision Tree Post Quiz

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Question 1 of 21

1. Question
What is a Decision Tree in machine learning?
- A graphical representation of all possible solutions to a decision based on certain conditions.
- A type of model that breaks down a dataset into smaller subsets while developing an associated decision tree.
- A fancy flowchart that helps make decisions based on certain rules.
- A detective algorithm that categorizes information based on learning.
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Question 2 of 21

2. Question
When is it recommended to use Decision Trees?
- When you have only a few factors to consider for decision-making.
- When you need to make decisions based on multiple factors.
- When you want to handle only numerical data.
- When you want to avoid classification tasks.
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Question 3 of 21

3. Question
What is a Root Node in a Decision Tree?
- The final node where the decision is made.
- The topmost node representing the entire dataset.
- The nodes that branch off from the root node.
- The nodes that ask further questions after the root node.
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Question 4 of 21

4. Question
What is Gini Impurity used for in Decision Trees?
- Measuring the probability of correct classification in a branch.
- Determining the best question to ask at each node.
- Calculating the reduction in entropy for each split.
- Evaluating the stability of the decision tree.
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Question 5 of 21

5. Question
What is the main advantage of Decision Trees over other classification algorithms?
- They are more accurate in predicting outcomes.
- They are simpler to understand and interpret.
- They are less prone to overfitting.
- They require minimal data preparation.
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Question 6 of 21

6. Question
What is a limitation of Decision Trees?
- They are non-parametric models.
- They are highly stable with small changes in data.
- They are biased towards features with fewer levels.
- They can overfit the training data.
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Question 7 of 21

7. Question
What is the Iris Dataset used for in the context of Decision Trees?
- To predict the species of Iris flowers based on four features.
- To classify animals based on their characteristics.
- To determine the weather conditions for a picnic.
- To analyze customer churn in a business.
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Question 8 of 21

8. Question
What is the purpose of the confusion matrix in evaluating a Decision Tree?
- To visualize the decision tree structure.
- To measure the reduction in entropy for each split.
- To show the correct and incorrect predictions made by the model.
- To determine the best split criteria.
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Question 9 of 21

9. Question
What does Precision measure in the classification report of a Decision Tree?
- How well the model finds all relevant cases within a category.
- How often the model is correct when it makes a prediction.
- The harmonic mean of precision and recall.
- The number of instances of each category in the dataset.
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Question 10 of 21

10. Question
Why is overfitting a common problem in Decision Trees?
- Because they are non-parametric models.
- Because they make assumptions about the underlying data distribution.
- Because they tend to memorize the training data instead of generalizing.
- Because they are stable with small changes in data.
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Question 11 of 21

11. Question
What is the advantage of Decision Trees in terms of data preparation?
- They require extensive data normalization.
- They can handle both numerical and categorical data without much preprocessing.
- They are influenced by outliers in the data.
- They make strong assumptions about the data distribution.
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Question 12 of 21

12. Question
What is the primary purpose of pruning in Decision Trees?
- To increase the complexity of the decision tree.
- To reduce the size of the tree and prevent overfitting.
- To introduce bias towards features with more levels.
- To create a diagonal decision boundary.
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Question 13 of 21

13. Question
How does a Decision Tree make decisions at each node?
- By choosing the question that gives the most useful information.
- By asking random questions without a specific order.
- By always splitting based on numerical data only.
- By choosing the most complex question available.
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Question 14 of 21

14. Question
What does the Information Gain measure in Decision Trees?
- The probability of correct classification in a branch.
- The reduction in entropy provided by a particular split.
- The number of correct predictions made by the model.
- The complexity of the decision tree.
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Question 15 of 21

15. Question
What is the purpose of the Root Node in a Decision Tree?
- To represent the entire dataset.
- To make the final decision.
- To branch off into further questions.
- To ask the first question at the top.
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Question 16 of 21

16. Question
How does a Decision Tree differ from other classification algorithms?
- It tends to overfit the training data more than other algorithms.
- It is more complex and harder to interpret.
- It separates categories clearly and is easier to understand.
- It is biased towards features with fewer levels.
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Question 17 of 21

17. Question
What is the primary role of Internal Nodes in a Decision Tree?
- To represent the entire dataset.
- To make the final decision.
- To branch off into further questions.
- To ask the first question at the top.
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Question 18 of 21

18. Question
How does Decision Trees handle numerical and categorical data?
- They can only work with numerical data.
- They require data normalization for all types of data.
- They can handle both numerical and categorical data without much preprocessing.
- They ignore categorical data in the decision-making process.
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Question 19 of 21

19. Question
What is the purpose of the Leaf Node in a Decision Tree?
- To represent the entire dataset.
- To make the final decision.
- To branch off into further questions.
- To ask the first question at the top.
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Question 20 of 21

20. Question
Why is it important to prune a Decision Tree?
- To increase the complexity of the decision tree.
- To reduce the size of the tree and prevent overfitting.
- To introduce bias towards features with more levels.
- To create a diagonal decision boundary.
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Question 21 of 21

21. Question
What is the purpose of Splitting Criteria in Decision Trees?
- To visualize the decision tree structure.
- To determine the best question to ask at each node.
- To show the correct and incorrect predictions made by the model.
- To evaluate the stability of the decision tree.
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