BADM 211 EXAM SET QUESTIONS AND
VERIFIED ANSWERS
◉ Reducing the number of predictors to the smallest set that will
still provide accurate predictions is a concept called
____________________.
regeneration
parsimony
shrinkage
Gillette's razor. Answer: parsimony
◉ You have been given a dataset with 15 predictors and a binary
outcome that denotes whether a customer has left the company (yes
or no). As an absolute minimum, you'll need _____________ samples to
achieve an minimally accurate prediction.
150
180
190
200. Answer: 180
,◉ You have been given a dataset with 15 predictors and a numeric
outcome that denotes the income that a household has obtained. As
an absolute minimum, you'll need _____________ samples to achieve an
minimally accurate prediction.
200
180
300
150. Answer: 150
◉ The process of identifying outliers is best performed by someone
with domain knowledge as opposed to someone with statistical
knowledge.
True
False. Answer: True
◉ If you impute a missing value with its column mean, then you will
___________________.
maximize the variability of the dataset
overweight the variability of the dataset
understate the variability of the dataset
,normalize the variability of the dataset. Answer: understate the
variability of the dataset
◉ Standardization uses the following formula:
Using the rule-of-thumb method, one can assume that all extreme
values (outliers) will be greater than ____________ or less than
____________.
0, 1
1, 0
+3, -3
+1, -1. Answer: +3, -3
◉ In contrast to standardization, normalization (i.e., MinMaxScaler
in sci-kit learn) fits all values between __________________.
-3 and +3
0 and 1
a lower and an upper boundary selected by the data analyst
-infinity, +infinity. Answer: a lower and an upper boundary selected
by the data analyst
, ◉ Overfitting occurs when ___________________ is low, which makes
______________ higher.
variance, bias
bias, variance
irreducible error, reducible error
sampling, accuracy. Answer: bias, variance
◉ As a means to control excessive bias, we can use ______________.
data partitions
dimension reduction
standardization
normalization. Answer: data partitions
◉ When dealing with a class imbalance in a classification model, the
data analyst can _____________ the minority class or ________________ the
majority class.
underweight, overweight
underweight, oversample
subsample, oversample
VERIFIED ANSWERS
◉ Reducing the number of predictors to the smallest set that will
still provide accurate predictions is a concept called
____________________.
regeneration
parsimony
shrinkage
Gillette's razor. Answer: parsimony
◉ You have been given a dataset with 15 predictors and a binary
outcome that denotes whether a customer has left the company (yes
or no). As an absolute minimum, you'll need _____________ samples to
achieve an minimally accurate prediction.
150
180
190
200. Answer: 180
,◉ You have been given a dataset with 15 predictors and a numeric
outcome that denotes the income that a household has obtained. As
an absolute minimum, you'll need _____________ samples to achieve an
minimally accurate prediction.
200
180
300
150. Answer: 150
◉ The process of identifying outliers is best performed by someone
with domain knowledge as opposed to someone with statistical
knowledge.
True
False. Answer: True
◉ If you impute a missing value with its column mean, then you will
___________________.
maximize the variability of the dataset
overweight the variability of the dataset
understate the variability of the dataset
,normalize the variability of the dataset. Answer: understate the
variability of the dataset
◉ Standardization uses the following formula:
Using the rule-of-thumb method, one can assume that all extreme
values (outliers) will be greater than ____________ or less than
____________.
0, 1
1, 0
+3, -3
+1, -1. Answer: +3, -3
◉ In contrast to standardization, normalization (i.e., MinMaxScaler
in sci-kit learn) fits all values between __________________.
-3 and +3
0 and 1
a lower and an upper boundary selected by the data analyst
-infinity, +infinity. Answer: a lower and an upper boundary selected
by the data analyst
, ◉ Overfitting occurs when ___________________ is low, which makes
______________ higher.
variance, bias
bias, variance
irreducible error, reducible error
sampling, accuracy. Answer: bias, variance
◉ As a means to control excessive bias, we can use ______________.
data partitions
dimension reduction
standardization
normalization. Answer: data partitions
◉ When dealing with a class imbalance in a classification model, the
data analyst can _____________ the minority class or ________________ the
majority class.
underweight, overweight
underweight, oversample
subsample, oversample
