A company wants to build a system that automatically classifies incoming customer emails as either "complaint," "inquiry," or "feedback" based on the words in the message. They are looking for a simple, fast solution that works well with large amounts of text data and doesn't require extensive training time. Which machine learning approach is most appropriate for this task?

Question

BlackTom AI · Accepted Answer

Consider the problem: automatically classifying incoming customer emails into categories like 'complaint,' 'inquiry,' or 'feedback' using text data, with a need for speed and scalability and minimal training time.
Option 1: Naïve Bayes, because it is a fast and effective classifier for text-based problems. This approach is well-suited for text classification because it treats documents as a bag of words, handles high-dimensional sparse features efficiently, and typically trains very quickly. It tends to perform well with relatively small amounts of labeled data and scales well to large corpora, which matches the scenario described.
Option 2: Support Vector Machine, because it models non-linear boundaries for images. While SVMs can be used for text classification, the justification here is inappropriate: the statement emphasizes non-linear boundaries for images, which is not aligned with the text classification context. In practice, SVMs can be slower to train on very large text datasets and may require careful kernel and parameter tuning, which conflicts with the desire for a simple and fast solution.
Option 3: K-Means Clustering, because it groups similar data without labels. K-Means is an unsupervised method and would not directly produce labeled categories like 'complaint,' 'inquiry,' or 'feedback' without additional labeling or post-processing. For a supervised classification task with predefined categories, this approach is not appropriate.
Option 4: Linear Regression, because it models the relationship between input and output values. Linear regression is designed for predicting continuous numerical values, not categorical labels. Using it for text classification would be ill-suited and would require workaround tricks, which would degrade performance and interpretability.
In summary, the statement in Option 1 aligns with the typical strengths of Naïve Bayes for text classification, while the other options either misalign with the supervised, labeled nature of the task or carry practical drawbacks for large-scale text data.

IS 4487-006 Fall 2025 Week 12 - Comprehension Quiz

View Explanation

Log in for full answers

Similar Questions

The supervised machine learning approach to sentiment analysis is based on:

Text classification utilizes a unique set of classification algorithms, distinct from algorithms used in predictive analytics with numeric data:

Which of the following is a better process for building a text classification model?

Which of the following is technically a text classification task?

In a consumer society, many adults channel creativity into buying things

Economic stress and unpredictable times have resulted in a booming industry for self-help products

People born without creativity never can develop it

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler