QMB3302 Final Exam ACTUAL QUESTIONS AND CORRECT ANSWERS

QMB3302 Final ExamACTUAL
QUESTIONS AND CORRECT ANSWERS
- Which of the following is a common use case for the random forest
algorithm in machine learning?
a. Classifying data into categories based on input features
b. Predicting a continuous target variable based on input features.
c. Clustering similar data points into groups.
d. Finding the optimal hyperparameters for a model. - CORRECT
ANSWERS a


Which of the following is a potential benefit of using decision trees in
machine learning?
a. Easy to overfit the data
b. Great at predicting future data
c. Can handle both numerical and categorical data.
d. Can only handle numerical data - CORRECT ANSWERS c


Which of the following statements best describes an ensemble method
in machine learning?
a. A technique that combines the results of multiple models to
improve overall predictive accuracy,
b. An algorithm that learns to find patterns and relationships in data
without being explicitly programmed,
c. A method that automatically groups similar data points into clusters
based on their characteristics.

,d. A model that predicts the value of a dependent variable based on
the values of one or more independent variables. - CORRECT
ANSWERS a


Which of the following best describes supervised learning?
a. A machine learning approach where the algorithm learns to
optimize a performance metric by adjusting its internal parameters.
b. A machine learning approach where the algorithm automatically
groups similar data points into clusters.
c. A machines learning approach where the algorithm receives labeled
data and learns to map inputs to outputs based on those labels.
d. A machine learning approach where the algorithm learns to find
patterns and relationships in data without being explicitly
programmed. - CORRECT ANSWERS c


Which of the following statements best describes classification in
machine learning?
a. A type of supervised learning where the goal is to predict a
continuous target based on input features.
b. A type of reinforcement learning where the goal is to learn an
optimal policy for making decisions in an environment.
c. A type of unsupervised learning where the goal is to group similar
data points into clusters.
d. A type of supervised learning where the goal is to assign input data
points to predefined categories or classes. - CORRECT ANSWERS
d


We want the R-squared value for our regression model to be 100%.

, a. True
b. Fales - CORRECT ANSWERS b


One weakness of cross-validation discussed is that information can
sometimes _______ across different periods. A common situation in
which this happens is when we are looking at stock data.
a. Leak
b. Overfit
c. Not leak
d. Underfit - CORRECT ANSWERS a


In which of these situations would you want to use a clustering
algorithm?
a. You were given the financial data for the Federal Reserve of New
York in 2023 and want to determine where the discrepancy in
accumulated depreciation came from before you submit the financial
statements.
b. You have a dataset containing past crimes of current defendants and
you want to determine the likelihood that they will commit another
crime.
c. You have a dataset containing 2023 Charlotte, NC housing data and
you want to predict 2024 housing prices.
d. You have a dataset containing set for Cheesecake Factory and you
want to look at customer spending at the restaurant in order to find
patterns among customers who share similar characteristics. -
CORRECT ANSWERS d