WGU C951 TASK 4: MACHINE
LEARNING PROJECT PROPOSAL FOR
EMAIL SPAM FILTERING
1. In the context of the C951 Task 4 proposal, what is the primary objective of implementing a
machine learning model for spam filtering?
A. To increase the total volume of emails received by the server.
B. To automatically categorize incoming emails as either ‘ham’ or ‘spam’ with high
precision.
C. To delete all emails containing attachments to prevent viruses.
D. To decrypt encrypted emails for security inspection.
Answer: B
Conceptual Explanation: The primary goal of a spam filtering project is classification:
identifying legitimate mail (ham) and unsolicited mail (spam) to improve user experience
and security.
,2. Which evaluation metric is most critical if the project goal is to avoid moving legitimate
business emails into the spam folder?
A. Recall
B. Accuracy
C. L1 Regularization
D. Precision
Answer: D
Conceptual Explanation: Precision measures the accuracy of positive predictions. High
precision ensures that very few legitimate emails (false positives) are incorrectly labeled as
spam.
3. When preparing text data for a spam filter, what is the purpose of ‘Stemming’?
A. To increase the weight of rare words in the corpus.
B. To remove numerical digits from the email body.
C. To convert words to their root form by removing suffixes.
D. To identify the language of the email.
Answer: C
Conceptual Explanation: Stemming reduces words to their base or root form (e.g.,
‘running’ to ‘run’) to consolidate the feature space in natural language processing.
, 4. Which algorithm is traditionally favored for text classification due to its assumption of
feature independence?
A. K-Means Clustering
B. Naive Bayes
C. Principal Component Analysis
D. Linear Regression
Answer: B
Conceptual Explanation: Naive Bayes is a probabilistic classifier based on Bayes’ Theorem
with an assumption of independence among predictors, making it highly effective for text
data.
5. What does the ‘TF’ in TF-IDF stand for in the context of feature extraction?
A. Term Frequency
B. Technical Factor
C. Total Frequency
D. True False
Answer: A
Conceptual Explanation: TF-IDF stands for Term Frequency-Inverse Document
Frequency, which reflects how important a word is to a document in a collection.
LEARNING PROJECT PROPOSAL FOR
EMAIL SPAM FILTERING
1. In the context of the C951 Task 4 proposal, what is the primary objective of implementing a
machine learning model for spam filtering?
A. To increase the total volume of emails received by the server.
B. To automatically categorize incoming emails as either ‘ham’ or ‘spam’ with high
precision.
C. To delete all emails containing attachments to prevent viruses.
D. To decrypt encrypted emails for security inspection.
Answer: B
Conceptual Explanation: The primary goal of a spam filtering project is classification:
identifying legitimate mail (ham) and unsolicited mail (spam) to improve user experience
and security.
,2. Which evaluation metric is most critical if the project goal is to avoid moving legitimate
business emails into the spam folder?
A. Recall
B. Accuracy
C. L1 Regularization
D. Precision
Answer: D
Conceptual Explanation: Precision measures the accuracy of positive predictions. High
precision ensures that very few legitimate emails (false positives) are incorrectly labeled as
spam.
3. When preparing text data for a spam filter, what is the purpose of ‘Stemming’?
A. To increase the weight of rare words in the corpus.
B. To remove numerical digits from the email body.
C. To convert words to their root form by removing suffixes.
D. To identify the language of the email.
Answer: C
Conceptual Explanation: Stemming reduces words to their base or root form (e.g.,
‘running’ to ‘run’) to consolidate the feature space in natural language processing.
, 4. Which algorithm is traditionally favored for text classification due to its assumption of
feature independence?
A. K-Means Clustering
B. Naive Bayes
C. Principal Component Analysis
D. Linear Regression
Answer: B
Conceptual Explanation: Naive Bayes is a probabilistic classifier based on Bayes’ Theorem
with an assumption of independence among predictors, making it highly effective for text
data.
5. What does the ‘TF’ in TF-IDF stand for in the context of feature extraction?
A. Term Frequency
B. Technical Factor
C. Total Frequency
D. True False
Answer: A
Conceptual Explanation: TF-IDF stands for Term Frequency-Inverse Document
Frequency, which reflects how important a word is to a document in a collection.
