Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
The loss function does not penalize false negatives effectively for that class.
B
The model has too many parameters leading to overfitting.
C
The data for that class is not representative of real-world scenarios.
D
The model lacks enough training data overall.
Understanding the Answer
Let's break down why this is correct
Answer
When a model keeps missing a particular class, it often means the loss function is not penalizing mistakes on that class enough, so the model learns to ignore it. In many training regimes, the cross‑entropy loss treats all classes equally, but if that class is rare or has noisy labels, the overall loss is dominated by the other, more frequent classes. As a result, the gradient signal for the minority class is weak, and the model never learns to distinguish it well. For example, if 90 % of the data are “dog” and only 10 % are “cat,” the loss will be driven mainly by dog errors, leaving cat predictions poorly calibrated. Thus, the most likely underlying cause is that the loss function does not properly weight or penalize misclassifications of that specific class.
Detailed Explanation
The loss function tells the model how bad a mistake is. Other options are incorrect because The idea that too many parameters cause the error is a common mistake; Thinking the data is the problem mixes data quality with the learning rule.
Key Concepts
Loss functions in classification
Classification evaluation metrics
Model training and generalization
Topic
Classification Summary
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
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Question 1If a machine learning model consistently underperforms on its predictions, which underlying factor is most likely contributing to this issue?
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Question 2If a multi-class classification model consistently yields high accuracy but performs poorly on a specific underrepresented class, what underlying issue might this indicate about the loss function used?
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3
Question 3When selecting a loss function for a multi-class classification task, which factor is most crucial for ensuring model performance?
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Question 4When selecting a loss function for a multi-class classification problem, which of the following considerations is most critical for aligning model performance with classification objectives?
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