If 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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The loss function is not penalizing errors on all classes equally.

The model complexity is too low to capture the data distribution.

The training data is too large, leading to overfitting.

The feature extraction method is not suitable for the task.

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A high overall accuracy with a weak underrepresented class suggests the loss function is treating all classes the same, so it focuses on the majority classes that dominate the error signal. In plain terms, the loss gives little weight to mistakes on the minority class, so the model learns to predict the common classes well and ignores the rare one. This imbalance in the loss surface causes the model to ignore the minority class during training. For example, if 90 % of the data is class A and 10 % is class B, a plain cross‑entropy loss will mainly penalize errors on class A, letting the model overlook class B. The fix is to use a weighted or focal loss that gives higher importance to the underrepresented class.

Detailed Explanation

The loss function gives more weight to errors on common classes. Other options are incorrect because Model complexity is about how well a model can fit data, not about how the loss treats classes; A larger training set usually reduces overfitting and improves generalization.

Key Concepts

Multi-class Classification

Loss Functions

Model Evaluation

Topic

Multi-class Loss Functions

Difficulty

medium level question

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understand

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