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Question & Answer
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The loss function does not penalize false negatives effectively for that class.
The model has too many parameters leading to overfitting.
The data for that class is not representative of real-world scenarios.
The model lacks enough training data overall.
Understanding the Answer
Let's break down why this is correct
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
Classification Summary
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Definition
A summary of key points related to loss functions and classification evaluation metrics. It emphasizes the importance of selecting appropriate loss functions that align with the classification objectives to improve model performance.
Topic Definition
A summary of key points related to loss functions and classification evaluation metrics. It emphasizes the importance of selecting appropriate loss functions that align with the classification objectives to improve model performance.
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