QMB3302 FINAL EXAM {LATEST 2026 -
2027 update} QUESTIONS AND ANSWERS
100% CORRECT
Decision tree's are nice because they are fairly simple and straightforward to interpret
(True of False) - correct answer True
When running our first decision tree, we took out "maxdepth=". This had the
unfortunate result of... - correct answer Building a very large hard to understand tree
What is the terminal node as discussed in the lecture? - correct answer The last node
(sometimes called a leaf), the tree doesnt split after this
Models, such as the random forest model we ran, often have a number of parameters
that the analyst can choose or set. What is a the best source of up to date information
,about the different parameters that can be set? - correct answer The scikit learn
documentation
Random forests are __________ interpretable than decision trees - correct answer
Less
Pipelines are useful (in the analytics with Python sense) for what reasons? - correct
answer Make it easy to repeat/replicate steps and run multiple models, help organize
the code you used to clean and treat data, and make it eassy to change small things in
model like which variables to include.
All the the nodes prior to the output nodes essentially 'guess' at the correct weights.
Then the algorithm checks to see if the initial guess is correct (usually not). When it is
wrong... - correct answer It tries again (runs another epoch)
, Neural networks are an unsupervised technique, because there is no target variable.
(True or False) - correct answer False
When viewing a diagram of a neural network there are several layers. The input layer:
- correct answer Are te Xs, or inputs from your data
When viewing a diagram of a neural network there are several layers. The Output
layer: - correct answer Are the Ys (The target variable you are interested in)
When viewing a diagram of a neural network there are several layers. The hidden
layer: - correct answer Something you dont see, here there is some computation to
transform X into the Y
NLP stands for - correct answer natural language processing
2027 update} QUESTIONS AND ANSWERS
100% CORRECT
Decision tree's are nice because they are fairly simple and straightforward to interpret
(True of False) - correct answer True
When running our first decision tree, we took out "maxdepth=". This had the
unfortunate result of... - correct answer Building a very large hard to understand tree
What is the terminal node as discussed in the lecture? - correct answer The last node
(sometimes called a leaf), the tree doesnt split after this
Models, such as the random forest model we ran, often have a number of parameters
that the analyst can choose or set. What is a the best source of up to date information
,about the different parameters that can be set? - correct answer The scikit learn
documentation
Random forests are __________ interpretable than decision trees - correct answer
Less
Pipelines are useful (in the analytics with Python sense) for what reasons? - correct
answer Make it easy to repeat/replicate steps and run multiple models, help organize
the code you used to clean and treat data, and make it eassy to change small things in
model like which variables to include.
All the the nodes prior to the output nodes essentially 'guess' at the correct weights.
Then the algorithm checks to see if the initial guess is correct (usually not). When it is
wrong... - correct answer It tries again (runs another epoch)
, Neural networks are an unsupervised technique, because there is no target variable.
(True or False) - correct answer False
When viewing a diagram of a neural network there are several layers. The input layer:
- correct answer Are te Xs, or inputs from your data
When viewing a diagram of a neural network there are several layers. The Output
layer: - correct answer Are the Ys (The target variable you are interested in)
When viewing a diagram of a neural network there are several layers. The hidden
layer: - correct answer Something you dont see, here there is some computation to
transform X into the Y
NLP stands for - correct answer natural language processing
