CS-7641 - MIDTERM UPDATED ACTUAL QUESTIONS AND
CORRECT ANSWERS
Question:
1. In general, when choosing a hypothesis space, is a very large hypothesis space preferable to a smaller
one?
Answer:
False
Question:
2. Can a network of perceptrons with linear activation functions be simplified into a single unit perceptron
computing the same function?
Answer:
True
Question:
3. Should the nearest neighbor method be used over a decision tree learning method for a learning problem
with over 1000 attributes, only a few of which are probably relevant?
Answer:
False
Question:
4. Is your target concept an element of your hypothesis space?
Answer:
True
Question:
5. Does the Boosting algorithm have the advantage of not overfitting?
Answer:
False
Question:
6. What are potential issues with very deep decision trees?
- Overfitting to training data
- Being insensitive to feature scaling
- Underfitting due to simplicity
- Reduced interpretability
- Long computation times during predictions
- Always providing the best accuracy
, Answer:
Correct:
- Reduced interpretability
- Long computation times during predictions
- Being insensitive to feature scaling
- Overfitting to training data Incorrect:
- Underfitting due to simplicity
- Always providing the best accuracy
Question:
7. When deciding on a split for a continuous variable in decision trees, what is true?
- The split always divides data into equal parts
- A threshold is determined for splitting instances into two groups
- The variable is always discretized into categories
- The split relies on a fixed global threshold for all nodes
- The split aims to increase the homogeneity of child nodes
- The data is often sorted by that variable's values
Answer:
Correct:
- A threshold is determined for splitting instances into two groups
- The split aims to increase the homogeneity of child nodes
- The data is often sorted by that variable's values Incorrect:
- The split always divides data into equal parts
- The split relies on a fixed global threshold for all nodes
- The variable is always discretized into categories
Question:
8. Why might pruning be applied to a decision tree?
- To ensure the tree is balanced
- To remove branches that provide little to no predictive power
- To simplify the model and improve interpretability
- To always achieve the best accuracy
- To increase tree depth
- To reduce overfitting
Answer:
Correct:
- To remove branches that provide little to no predictive power
- To simplify the model and improve interpretability
- To reduce overfitting Incorrect:
- To increase tree depth
- To always achieve the best accuracy
- To ensure the tree is balanced
CORRECT ANSWERS
Question:
1. In general, when choosing a hypothesis space, is a very large hypothesis space preferable to a smaller
one?
Answer:
False
Question:
2. Can a network of perceptrons with linear activation functions be simplified into a single unit perceptron
computing the same function?
Answer:
True
Question:
3. Should the nearest neighbor method be used over a decision tree learning method for a learning problem
with over 1000 attributes, only a few of which are probably relevant?
Answer:
False
Question:
4. Is your target concept an element of your hypothesis space?
Answer:
True
Question:
5. Does the Boosting algorithm have the advantage of not overfitting?
Answer:
False
Question:
6. What are potential issues with very deep decision trees?
- Overfitting to training data
- Being insensitive to feature scaling
- Underfitting due to simplicity
- Reduced interpretability
- Long computation times during predictions
- Always providing the best accuracy
, Answer:
Correct:
- Reduced interpretability
- Long computation times during predictions
- Being insensitive to feature scaling
- Overfitting to training data Incorrect:
- Underfitting due to simplicity
- Always providing the best accuracy
Question:
7. When deciding on a split for a continuous variable in decision trees, what is true?
- The split always divides data into equal parts
- A threshold is determined for splitting instances into two groups
- The variable is always discretized into categories
- The split relies on a fixed global threshold for all nodes
- The split aims to increase the homogeneity of child nodes
- The data is often sorted by that variable's values
Answer:
Correct:
- A threshold is determined for splitting instances into two groups
- The split aims to increase the homogeneity of child nodes
- The data is often sorted by that variable's values Incorrect:
- The split always divides data into equal parts
- The split relies on a fixed global threshold for all nodes
- The variable is always discretized into categories
Question:
8. Why might pruning be applied to a decision tree?
- To ensure the tree is balanced
- To remove branches that provide little to no predictive power
- To simplify the model and improve interpretability
- To always achieve the best accuracy
- To increase tree depth
- To reduce overfitting
Answer:
Correct:
- To remove branches that provide little to no predictive power
- To simplify the model and improve interpretability
- To reduce overfitting Incorrect:
- To increase tree depth
- To always achieve the best accuracy
- To ensure the tree is balanced
