CS-7641 – MIDTERM EXAM PREP
2026/2027 | COMPLETE REVIEW GUIDE
In general, when choosing a hypothesis space, is a very large hypothesis space
preferable to a smaller one? - correct answer-False
Can a network of perceptrons with linear activation functions be simplified into a
single unit perceptron computing the same function? - correct answer-True
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? - correct answer-False
Is your target concept an element of your hypothesis space? - correct answer-
True
Does the Boosting algorithm have the advantage of not overfitting? - correct
answer-False
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 - correct 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
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 - correct 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
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 - correct 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
Which of the following are ensemble methods used in machine learning for
improving model accuracy and robustness?
- Random Forest
- Simple Linear Regression
2026/2027 | COMPLETE REVIEW GUIDE
In general, when choosing a hypothesis space, is a very large hypothesis space
preferable to a smaller one? - correct answer-False
Can a network of perceptrons with linear activation functions be simplified into a
single unit perceptron computing the same function? - correct answer-True
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? - correct answer-False
Is your target concept an element of your hypothesis space? - correct answer-
True
Does the Boosting algorithm have the advantage of not overfitting? - correct
answer-False
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 - correct 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
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 - correct 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
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 - correct 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
Which of the following are ensemble methods used in machine learning for
improving model accuracy and robustness?
- Random Forest
- Simple Linear Regression
