DATA 5600 - Final Exam Questions and
Correct Answers
Question 1
What are the common ways of reducing the overfitting in a machine learning
exercise?
a. Collecting more data
b. Complexity reduction (ex regularization)
c. Using cross validation
d. All of the above
Correct Answer
All of the above
Question 2
What are the two main types of unsupervised learning algorithms?
a. Dimension reduction algorithm and Clustering techniques
b. Regression and Classification
c. Supervised and Reinforcement learning
d. None of the above
Correct Answer
Dimension reduction algorithm and Clustering techniques
Question 3
In this type of penalized regression, we can force some of the coefficient estimates to
be exactly zero. In other words, this model allows feature selection.
a. Ridge and LASSO
b. Ridge and Elastic Net
c. LASSO and Elastic Net
d. only LASSO
Correct Answer
LASSO and Elastic Net
Page 1 of 47
,Question 4
The main idea of regularization in machine learning is to introduce a small amount of
bias to the model by making it ------------------ in order to get a significant reduction
in ----------------.
a. less flexible - model variance
b. more flexible - model variance
c. more flexible - model bias
d. less flexible - model bias
Correct Answer
less flexible - model variance
Question 5
The goal of unsupervised learning is to discover the underlying patterns and find
groups of samples that behave similarly!
True or False
Correct Answer
True
Question 6
Which of the following models is not an example of penalized regression?
a. Ridge regression
b. Lasso regression
c. Polynomial regression
d. Elastic net regression
Correct Answer
Polynomial regression
Page 2 of 47
,Question 7
What is a potential disadvantage of using XGBoost?
a. XGBoost has a higher risk of overfitting compared to other frameworks.
b. XGBoost cannot handle large datasets.
c. XGBoost is less accurate than other frameworks.
d. XGBoost is not compatible with Python.
Correct Answer
XGBoost has a higher risk of overfitting compared to other frameworks.
Question 8
Which of the following statements accurately describes the cost complexity pruning
method in decision trees?
a. Cost complexity pruning involves randomly removing branches from a decision tree
until a desired level of accuracy is achieved.
b. Cost complexity pruning involves adding more branches to a decision tree to
improve its predictive accuracy.
c. Cost complexity pruning involves scaling the weights of features in a decision tree
to adjust their importance in the final prediction.
d. Cost complexity pruning involves adjusting the complexity of a decision tree by
adding or removing branches to minimize a cost function that balances between
model complexity and goodness of fit.
Correct Answer
Cost complexity pruning involves adjusting the complexity of a decision tree by
adding or removing branches to minimize a cost function that balances between
model complexity and goodness of fit.
Page 3 of 47
, Question 9
In a supervised machine learning algorithm, as we increase model complexity, the bias
---------- and the model variance ------------.
a. increase - increase
b. decrease - decrease
c. increase - decrease
d. decrease - increase
Correct Answer
decrease - increase
Question 10
What do the eigenvectors of the covariance matrix represent in the context of
Principal Component Analysis (PCA)?
a. The directions of the new feature space
b. The magnitude of the new feature space
c. The variance of the data along the new feature axes
d. The classification of data points in the dataset
Correct Answer
The directions of the new feature space
Question 11
Which of the following statements is true regarding feature importance in Random
Forests?
a. Feature importance refers to techniques that assign a score to input features based
on how useful they are at predicting a target variable.
b. Feature importance scores are assigned based on the depth of a feature in the tree.
c. Feature importance scores are assigned based on the correlation between a feature
and the target variable.
d. Feature importance scores are assigned based on the number of observations in
each node that use a particular feature.
Correct Answer
Feature importance refers to techniques that assign a score to input features based
on how useful they are at predicting a target variable.
Page 4 of 47
Correct Answers
Question 1
What are the common ways of reducing the overfitting in a machine learning
exercise?
a. Collecting more data
b. Complexity reduction (ex regularization)
c. Using cross validation
d. All of the above
Correct Answer
All of the above
Question 2
What are the two main types of unsupervised learning algorithms?
a. Dimension reduction algorithm and Clustering techniques
b. Regression and Classification
c. Supervised and Reinforcement learning
d. None of the above
Correct Answer
Dimension reduction algorithm and Clustering techniques
Question 3
In this type of penalized regression, we can force some of the coefficient estimates to
be exactly zero. In other words, this model allows feature selection.
a. Ridge and LASSO
b. Ridge and Elastic Net
c. LASSO and Elastic Net
d. only LASSO
Correct Answer
LASSO and Elastic Net
Page 1 of 47
,Question 4
The main idea of regularization in machine learning is to introduce a small amount of
bias to the model by making it ------------------ in order to get a significant reduction
in ----------------.
a. less flexible - model variance
b. more flexible - model variance
c. more flexible - model bias
d. less flexible - model bias
Correct Answer
less flexible - model variance
Question 5
The goal of unsupervised learning is to discover the underlying patterns and find
groups of samples that behave similarly!
True or False
Correct Answer
True
Question 6
Which of the following models is not an example of penalized regression?
a. Ridge regression
b. Lasso regression
c. Polynomial regression
d. Elastic net regression
Correct Answer
Polynomial regression
Page 2 of 47
,Question 7
What is a potential disadvantage of using XGBoost?
a. XGBoost has a higher risk of overfitting compared to other frameworks.
b. XGBoost cannot handle large datasets.
c. XGBoost is less accurate than other frameworks.
d. XGBoost is not compatible with Python.
Correct Answer
XGBoost has a higher risk of overfitting compared to other frameworks.
Question 8
Which of the following statements accurately describes the cost complexity pruning
method in decision trees?
a. Cost complexity pruning involves randomly removing branches from a decision tree
until a desired level of accuracy is achieved.
b. Cost complexity pruning involves adding more branches to a decision tree to
improve its predictive accuracy.
c. Cost complexity pruning involves scaling the weights of features in a decision tree
to adjust their importance in the final prediction.
d. Cost complexity pruning involves adjusting the complexity of a decision tree by
adding or removing branches to minimize a cost function that balances between
model complexity and goodness of fit.
Correct Answer
Cost complexity pruning involves adjusting the complexity of a decision tree by
adding or removing branches to minimize a cost function that balances between
model complexity and goodness of fit.
Page 3 of 47
, Question 9
In a supervised machine learning algorithm, as we increase model complexity, the bias
---------- and the model variance ------------.
a. increase - increase
b. decrease - decrease
c. increase - decrease
d. decrease - increase
Correct Answer
decrease - increase
Question 10
What do the eigenvectors of the covariance matrix represent in the context of
Principal Component Analysis (PCA)?
a. The directions of the new feature space
b. The magnitude of the new feature space
c. The variance of the data along the new feature axes
d. The classification of data points in the dataset
Correct Answer
The directions of the new feature space
Question 11
Which of the following statements is true regarding feature importance in Random
Forests?
a. Feature importance refers to techniques that assign a score to input features based
on how useful they are at predicting a target variable.
b. Feature importance scores are assigned based on the depth of a feature in the tree.
c. Feature importance scores are assigned based on the correlation between a feature
and the target variable.
d. Feature importance scores are assigned based on the number of observations in
each node that use a particular feature.
Correct Answer
Feature importance refers to techniques that assign a score to input features based
on how useful they are at predicting a target variable.
Page 4 of 47
