QMB3302 Final Exam ACTUAL
QUESTIONS AND CORRECT ANSWERS
- Pipelines are useful (in analytics with Python sense) for the
following reasons? (choose all that apply)
- Pipelines make it easy to repeat/replicate steps and run multiple
models
- Pipelines are good for moving data into your programming
environment
- Pipelines automatically update to new versions of Python
- Pipelines help organize code you used to clean and treat your data
- Pipelines make it very easy to change small things in your model,
like which variable to include - CORRECT ANSWERS -
Pipelines make it easy to repeat/replicate steps and run multiple
models
- Pipelines help organize code you used to clean and treat your data
- Pipelines make it very easy to change small things in your model,
like which variable to include
The basic idea of a regression is very simple. We have some X value
(which we call ______) and some Y value that we are trying to _____.
We could have multiple Y value, but that is not something we have
covered. - CORRECT ANSWERS features; predict
Y and y-hat are a little different. Y is our target vector, and y-hat is an
output in our model that is a.... (choose one of the following)
- estimate or prediction of y
- the actual value of y
, - an axis on our 2 way graph
- a combination of XY intercept coordinates - CORRECT ANSWERS
estimate or prediction of y
When looking at the code in the videos, we sometimes used a variable
to hold out model. What is the significance of the word "model" in the
below code?
model = LinearRegression(fit_intercept=True) - CORRECT
ANSWERS 'model' is a named variable and is just holding our
linear regression model. It could be renamed anything. The word itself
is not important. It is just a container.
What is a good model fit value? - CORRECT ANSWERS
unknowable without knowing/understanding the context of the
domain
Imagine X in the below is a missing value. If I were to run a median
imputer on this set of data, what would the return value be?
50, 60, 70, 80, 100, 60, 5000, X - CORRECT ANSWERS 70
Which of the below were discussed as being problems with the
holdout method for validation?
- Data is not available for test and control differences
- Outliers can skew the result
- The model is not trained on all of the data
QUESTIONS AND CORRECT ANSWERS
- Pipelines are useful (in analytics with Python sense) for the
following reasons? (choose all that apply)
- Pipelines make it easy to repeat/replicate steps and run multiple
models
- Pipelines are good for moving data into your programming
environment
- Pipelines automatically update to new versions of Python
- Pipelines help organize code you used to clean and treat your data
- Pipelines make it very easy to change small things in your model,
like which variable to include - CORRECT ANSWERS -
Pipelines make it easy to repeat/replicate steps and run multiple
models
- Pipelines help organize code you used to clean and treat your data
- Pipelines make it very easy to change small things in your model,
like which variable to include
The basic idea of a regression is very simple. We have some X value
(which we call ______) and some Y value that we are trying to _____.
We could have multiple Y value, but that is not something we have
covered. - CORRECT ANSWERS features; predict
Y and y-hat are a little different. Y is our target vector, and y-hat is an
output in our model that is a.... (choose one of the following)
- estimate or prediction of y
- the actual value of y
, - an axis on our 2 way graph
- a combination of XY intercept coordinates - CORRECT ANSWERS
estimate or prediction of y
When looking at the code in the videos, we sometimes used a variable
to hold out model. What is the significance of the word "model" in the
below code?
model = LinearRegression(fit_intercept=True) - CORRECT
ANSWERS 'model' is a named variable and is just holding our
linear regression model. It could be renamed anything. The word itself
is not important. It is just a container.
What is a good model fit value? - CORRECT ANSWERS
unknowable without knowing/understanding the context of the
domain
Imagine X in the below is a missing value. If I were to run a median
imputer on this set of data, what would the return value be?
50, 60, 70, 80, 100, 60, 5000, X - CORRECT ANSWERS 70
Which of the below were discussed as being problems with the
holdout method for validation?
- Data is not available for test and control differences
- Outliers can skew the result
- The model is not trained on all of the data
