QMB Final Exam
Week 8) Regression - Answer-Trying to find relationship between to variables X and Y
The basic idea of a regression is very simple. We have some X values we called these ___________ and
some Y value this is the variable we are trying to ________ . We could have multiple Y values, but that is
not something we have covered. - Answer-X values called features, and Y values trying to 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..... -
Answer-estimate or prediction of Y
When looking at the code in the videos, we sometimes used a variable to hold our model.
What is the significance of the word "model" in the below code?
model = LinearRegression(fit_intercept=True) - Answer-'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? - Answer-Unknowable without knowing/understanding the context and
the domain.
, Which of the below were discussed as being problems with the hold out method for validation? -
Answer--Outliers can skew the results
-The model is not trained on all of the day
What is hold out validation? - Answer-The holdout method is the simplest kind of cross validation. The
data set is separated into two sets, called the training set and the testing set. The function approximator
fits a function using the training set only.
Week 9) What is the first variable in a decision tree called (before any of the branches)? - Answer-Root
One problem with a decision tree is that you are prone to ___________ if you are not careful or do not
set the __________ appropriately. - Answer-Overfitting; Max Depth
What is a random forest? - Answer-A random forest is simply a collection of decision trees whose results
are aggregated into one final result.
The random forest algorithm prevents, or at least avoids to some extent, the problems with overfitting
found in decision trees. T/F - Answer-True
Random Forests can only be used on classification problems. T/F - Answer-False (Random forests has
application in regression and classification.)
-Random forest can handle both regression and classification tasks with a high degree of accuracy,
In order to interpret Decision Tree's it is necessary to first run a linear regression. T/F - Answer-False
Decision tree's are nice because they are fairly simple and straightforward to interpret. T/F - Answer-
True
Week 8) Regression - Answer-Trying to find relationship between to variables X and Y
The basic idea of a regression is very simple. We have some X values we called these ___________ and
some Y value this is the variable we are trying to ________ . We could have multiple Y values, but that is
not something we have covered. - Answer-X values called features, and Y values trying to 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..... -
Answer-estimate or prediction of Y
When looking at the code in the videos, we sometimes used a variable to hold our model.
What is the significance of the word "model" in the below code?
model = LinearRegression(fit_intercept=True) - Answer-'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? - Answer-Unknowable without knowing/understanding the context and
the domain.
, Which of the below were discussed as being problems with the hold out method for validation? -
Answer--Outliers can skew the results
-The model is not trained on all of the day
What is hold out validation? - Answer-The holdout method is the simplest kind of cross validation. The
data set is separated into two sets, called the training set and the testing set. The function approximator
fits a function using the training set only.
Week 9) What is the first variable in a decision tree called (before any of the branches)? - Answer-Root
One problem with a decision tree is that you are prone to ___________ if you are not careful or do not
set the __________ appropriately. - Answer-Overfitting; Max Depth
What is a random forest? - Answer-A random forest is simply a collection of decision trees whose results
are aggregated into one final result.
The random forest algorithm prevents, or at least avoids to some extent, the problems with overfitting
found in decision trees. T/F - Answer-True
Random Forests can only be used on classification problems. T/F - Answer-False (Random forests has
application in regression and classification.)
-Random forest can handle both regression and classification tasks with a high degree of accuracy,
In order to interpret Decision Tree's it is necessary to first run a linear regression. T/F - Answer-False
Decision tree's are nice because they are fairly simple and straightforward to interpret. T/F - Answer-
True
