Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
The choice of an inappropriate loss function
B
Overfitting to the training data
C
Lack of training data variety
D
The model architecture is too complex
Understanding the Answer
Let's break down why this is correct
Answer
When a model keeps underperforming, the most common culprit is a mismatch between the loss function and what the task actually requires. If the loss does not reflect the real goal—such as using mean squared error for a classification problem—training will focus on minimizing the wrong quantity and the model will learn the wrong patterns. The optimizer will improve the loss but not the desired metric, so predictions stay weak. For example, training a classifier with a regression loss like MSE will push the network toward average outputs rather than confident class labels, leaving accuracy low. Adjusting the loss to match the evaluation metric usually restores performance.
Detailed Explanation
The loss function tells the model what to improve. Other options are incorrect because Overfitting means the model is too tuned to the training data; Having few data types can hurt accuracy, but it is not the main reason for a model that never improves.
Key Concepts
Loss Functions
Model Evaluation
Overfitting
Topic
Loss Functions
Difficulty
medium level question
Cognitive Level
understand
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