BADM 211 - EXAM 2 QUESTIONS & ANSWERS
Multiple linear regression is an algorithm for _________________ problems - Answers
:Supervised Learning
What could be the objectives behind fitting a regression model?
1) Explanatory or descriptive: assessing the average impact of inputs on an outcome
2) Explanatory or descriptive: given a set of input values for the observation, predicting
the outcome variable for the observations
3) Predictive: given a set of input values for the observation, predicting the outcome
variable for the observations - Answers :1 and 3
Which of the following is/are true in the context of Linear Regression models?
1) It fits a relationship between multiple numerical outcomes variables and a set of
predictors at once.
2) It fits a relationship between a numerical outcome and a set of predictors.
3) It attempts to fit multiple relationships between a numerical outcome and a set of
predictors simultaneously. - Answers :Only 2
The Python command "pd.get_dummies" ______. - Answers :Converts categorical
predictors into dummy variables
You calculate the RMSE for model A and B and found out they are 700 and 1100
respectively. Then - Answers :A has better predictive performance than B
Which of these statements are true?
1) Interpreting regression coefficients plays an important role in predictive modeling
2)Interpreting regression coefficients plays an important role in explanatory modeling -
Answers :Only 2
Recall that for a record k, we mathematically define error as e_k=y-y_p, where y is the
actual outcome and y_p is the predicted outcome. So, what does a negative error for
record k imply? - Answers :We are overestimating the outcome for record k
Which of the following is not a valid measure for quantifying predictive performance? -
Answers :Mean Error
Classification and prediction constitute: - Answers :Predictive analytics
Which of the following are types of categorical variables?
A) Ordinal
B) Nominal
C) Discrete - Answers :A and B
, Outcome variable is relevant in the case of _________.
A) Unsupervised Learning
B) Prediction
C) Classification - Answers :Both B and C
Which of the following is an example of supervised learning? - Answers :A store
manager attempting to predict sales for tomorrow using a model.
Classification and prediction are two types of UNsupervised learning. - Answers :False,
in both the problems an outcome variable is specified
Which statement is true regarding prediction and classification problems? - Answers
:Predicting whether it would rain tomorrow is an example of classification problem.
Association rules is an example of ____________________ . - Answers :Unsupervised
Learning
Three Possible data mining outcomes - Answers :descriptive (what happened)
predictive (what could happen)
prescriptive (what should we do)
Records(observations)(rows) - Answers :Variables(features)(columns)
Ways to Rescale - Answers :Standardize and Normalize
Standardize - Answers :by subtracting mean from each column and
then dividing by the standard deviation.
Normalize - Answers :by subtracting min and divide by max-min for each variable
Supervised Learning - Answers :Regression, Classification, Prediction
Which of the following is an
example of classifcation? - Answers :Deciding on whether to approve a customer for a
credit card
Which of the following is an example of
Unsupervised Learning? - Answers :Association Rules
Which of the following best
describes overfitting: - Answers :Thea model fit is excellent on the training data but the
prediction on unseen data is poor
Which model to pick? - Answers :Training Performance has some error and low
validation performance
Multiple linear regression is an algorithm for _________________ problems - Answers
:Supervised Learning
What could be the objectives behind fitting a regression model?
1) Explanatory or descriptive: assessing the average impact of inputs on an outcome
2) Explanatory or descriptive: given a set of input values for the observation, predicting
the outcome variable for the observations
3) Predictive: given a set of input values for the observation, predicting the outcome
variable for the observations - Answers :1 and 3
Which of the following is/are true in the context of Linear Regression models?
1) It fits a relationship between multiple numerical outcomes variables and a set of
predictors at once.
2) It fits a relationship between a numerical outcome and a set of predictors.
3) It attempts to fit multiple relationships between a numerical outcome and a set of
predictors simultaneously. - Answers :Only 2
The Python command "pd.get_dummies" ______. - Answers :Converts categorical
predictors into dummy variables
You calculate the RMSE for model A and B and found out they are 700 and 1100
respectively. Then - Answers :A has better predictive performance than B
Which of these statements are true?
1) Interpreting regression coefficients plays an important role in predictive modeling
2)Interpreting regression coefficients plays an important role in explanatory modeling -
Answers :Only 2
Recall that for a record k, we mathematically define error as e_k=y-y_p, where y is the
actual outcome and y_p is the predicted outcome. So, what does a negative error for
record k imply? - Answers :We are overestimating the outcome for record k
Which of the following is not a valid measure for quantifying predictive performance? -
Answers :Mean Error
Classification and prediction constitute: - Answers :Predictive analytics
Which of the following are types of categorical variables?
A) Ordinal
B) Nominal
C) Discrete - Answers :A and B
, Outcome variable is relevant in the case of _________.
A) Unsupervised Learning
B) Prediction
C) Classification - Answers :Both B and C
Which of the following is an example of supervised learning? - Answers :A store
manager attempting to predict sales for tomorrow using a model.
Classification and prediction are two types of UNsupervised learning. - Answers :False,
in both the problems an outcome variable is specified
Which statement is true regarding prediction and classification problems? - Answers
:Predicting whether it would rain tomorrow is an example of classification problem.
Association rules is an example of ____________________ . - Answers :Unsupervised
Learning
Three Possible data mining outcomes - Answers :descriptive (what happened)
predictive (what could happen)
prescriptive (what should we do)
Records(observations)(rows) - Answers :Variables(features)(columns)
Ways to Rescale - Answers :Standardize and Normalize
Standardize - Answers :by subtracting mean from each column and
then dividing by the standard deviation.
Normalize - Answers :by subtracting min and divide by max-min for each variable
Supervised Learning - Answers :Regression, Classification, Prediction
Which of the following is an
example of classifcation? - Answers :Deciding on whether to approve a customer for a
credit card
Which of the following is an example of
Unsupervised Learning? - Answers :Association Rules
Which of the following best
describes overfitting: - Answers :Thea model fit is excellent on the training data but the
prediction on unseen data is poor
Which model to pick? - Answers :Training Performance has some error and low
validation performance
