CS-7641 –Midterm Exam ACTUAL
QUESTIONS AND CORRECT ANSWERS
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
- Gradient Boosting Machines (GBM)
- Logistic Regression
- K-Means Clustering
- Support Vector Machines (SVMs) - CORRECT ANSWER Correct:
- Random Forest
- Gradient Boosting Machines (GBM)
Incorrect:
- Simple Linear Regression
- K-Means Clustering
- Logistic Regression
- Support Vector Machines (SVMs)
Which algorithms are primarily used for classification tasks?
- Polynomial Regression
- Decision Trees
- LASSO Regression
- Ridge Regression
- Linear Regression
- Support Vector Machines (SVM) - CORRECT ANSWER Correct:
- Decision Trees
- Support Vector Machines (SVM)
Incorrect:
QUESTIONS AND CORRECT ANSWERS
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
- Gradient Boosting Machines (GBM)
- Logistic Regression
- K-Means Clustering
- Support Vector Machines (SVMs) - CORRECT ANSWER Correct:
- Random Forest
- Gradient Boosting Machines (GBM)
Incorrect:
- Simple Linear Regression
- K-Means Clustering
- Logistic Regression
- Support Vector Machines (SVMs)
Which algorithms are primarily used for classification tasks?
- Polynomial Regression
- Decision Trees
- LASSO Regression
- Ridge Regression
- Linear Regression
- Support Vector Machines (SVM) - CORRECT ANSWER Correct:
- Decision Trees
- Support Vector Machines (SVM)
Incorrect:
