GT Students Final Quiz Overview
ISYE6501x Courseware edX ||
complete comprehensive questions
and correct verified answers ||
100% guaranteed pass
1. Select the type of problem that linear regression is best suited for.
A) Classification
B) Clustering
C) Experimental design
D) Prediction from feature data
Correct Answer: D
Rationale: Linear regression models the relationship between a
continuous dependent variable and one or more independent (feature)
variables. It is best suited for predicting a numerical outcome based on
, feature data. While it can be used for time-series with time as a feature,
its primary application is with feature data .
2. Select the type of problem that logistic regression is best suited for.
A) Classification
B) Clustering
C) Experimental design
D) Prediction from feature data
Correct Answer: A
Rationale: Logistic regression is used for binary classification problems
where the outcome is categorical (e.g., yes/no, spam/not spam). It
models the probability of a positive outcome using feature data. While it
can be extended to multi-class problems, its primary use is classification
with feature data .
3. Select the type of problem that k-means is best suited for.
, A) Classification
B) Clustering
C) Experimental design
D) Prediction from feature data
Correct Answer: B
Rationale: k-means is an unsupervised learning algorithm that partitions
data into k distinct clusters based on similarity (distance). It is best suited
for clustering unlabeled data to discover natural groupings. It is NOT
used for classification (requires labels) or prediction .
4. Select the type of problem that support vector machine (SVM) is best
suited for.
A) Classification
B) Clustering
ISYE6501x Courseware edX ||
complete comprehensive questions
and correct verified answers ||
100% guaranteed pass
1. Select the type of problem that linear regression is best suited for.
A) Classification
B) Clustering
C) Experimental design
D) Prediction from feature data
Correct Answer: D
Rationale: Linear regression models the relationship between a
continuous dependent variable and one or more independent (feature)
variables. It is best suited for predicting a numerical outcome based on
, feature data. While it can be used for time-series with time as a feature,
its primary application is with feature data .
2. Select the type of problem that logistic regression is best suited for.
A) Classification
B) Clustering
C) Experimental design
D) Prediction from feature data
Correct Answer: A
Rationale: Logistic regression is used for binary classification problems
where the outcome is categorical (e.g., yes/no, spam/not spam). It
models the probability of a positive outcome using feature data. While it
can be extended to multi-class problems, its primary use is classification
with feature data .
3. Select the type of problem that k-means is best suited for.
, A) Classification
B) Clustering
C) Experimental design
D) Prediction from feature data
Correct Answer: B
Rationale: k-means is an unsupervised learning algorithm that partitions
data into k distinct clusters based on similarity (distance). It is best suited
for clustering unlabeled data to discover natural groupings. It is NOT
used for classification (requires labels) or prediction .
4. Select the type of problem that support vector machine (SVM) is best
suited for.
A) Classification
B) Clustering
