Exam (elaborations) Machine Learning-

6/20/25, 7:16 PM




Machine Learning- Exam 1 TEST BANK EXAM 2025-2026
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univariate multivariable A model with one outcome and several explanatory variables
regression (most common).
univariate univariable One outcome, one explanatory variable (often used as the
regression introdocutory).
multivariate multivariable multiple outcomes, multiple explanatory variables.
regression
multivariate univariable multiple outcomes, single explanatory variable.
regression
multiple independent multiple regression or multi-variable regression
variables indicate
multiple dependent variables multi-variate regression
In a medical trial, we train A. it is a multivariate multivariable (multiple) regression.
a model with weight, age,
and race features, and we
get predictions for
variables blood pressure
and cholesterol with the
same model. Which of the
following is true?
a. It is a multivariate
multivariable (multiple)
regression
b. It is a univariate
multivariable (multiple)
regression
c. It is a multivariate univariable
regression
d. None of the options listed.



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A univariable regression uses b. One feature to predict the outcome
a. Two features to predict the
outcome
b. One feature to predict the
outcome
c. One feature in its dataset

d. At least two features in its
dataset
A performance measure for c. Root Square Mean Error (RSME)
regression is:
a. recall

b. precision

c. Root Square Mean Error
(RSME)
d. F1-score

(One-variable regression) theta0 = y-intercept, theta1 = slope
Consider the plot below
corresponding to h(x) =
theta0 +
theta1x What are theta0 and
theta1?
Root Mean Square Error b. error
(RMSE) is a measure of
how much
___________________ th
e
system typically makes in its
predictions
a. confidence

b. error

c. bias

d. variance

Mean Absolute Error is a c. Outliers
preferred performance
measure for data with many
a. Instances

b. Features

c. Outliers

d. Classes

feature scaling technique; values are shifted and
During the normalization/min-
max rescaled so that they end up ranging from 0 to 1.


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feature scaling, we subtract the mean value and then it
During standardization
divides by the standard deviation so that the resulting
distribution has unit variance.
For the following group of 0.02, 0.04, 0.08, 0.1, 0.2, 0.22 or find percentage of total.
data: 200, 400, 800, 1000,
2000, 2200, scale them with
min- max.
If you are using a learning c. xi = (age of house - 10)/25
algorithm to estimate the
price of houses in a city, you
may want one of your features
xi to capture age of the
houses. In your training set,
all the houses have an age
between 10 to 35 with an
average of 17. Which of the
following would you use as
features if you use
normalization for feature
scaling:
a. xi = age of house

b. xi = (age of house)/35

c. xi = (age of house - 10)/25
d. xi = (age of house - 17)/25

We would like to predict the Normalization = rescaled from 0 to 1.
grade that students would Standardization = subtract mean value (3.1) and divide by
standard deviation.
get in Machine Learning
class based on the following
features: GPA, study_hours,
math_grade,
programing_grade
We notice that the minimum
GPA is 1.5 and max GPA is
4.0 and the mean value is
3.1. How would you apply
standardization and
normalization for scaling
the GPA feature?
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