Learning Path
Question & Answer1
Understand Question2
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A
Error Rate
B
Precision
C
Recall
D
F1 Score
Understanding the Answer
Let's break down why this is correct
Answer
In supervised classification, the loss function is used during training to guide weight updates, while the metric is used after training to evaluate performance. A common choice is the cross‑entropy loss, which measures how well the predicted probabilities match the true labels; it encourages the model to assign high probability to the correct class. After training, the most common metric to report is accuracy, which simply counts the fraction of correctly classified samples. For example, if a model predicts the correct label for 8 out of 10 images, the accuracy is 80 % and the cross‑entropy loss would have been minimized during training to reach that result. Thus, cross‑entropy loss pairs naturally with accuracy as the classification metric.
Detailed Explanation
The F1 Score is a single number that shows how well a model balances catching true positives (precision) and finding all positives (recall). Other options are incorrect because Error Rate tells you how many predictions are wrong, but it does not combine precision and recall; Precision measures only how many of the predicted positives are correct.
Key Concepts
Loss Functions
Classification Metrics
Model Evaluation
Topic
Classification Summary
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1Which of the following loss functions are suitable for evaluating the performance of multi-class classification models? Select all that apply.
mediumComputer-science
Practice
2
Question 2Which of the following statements about loss functions and classification evaluation metrics are correct? Select all that apply.
easyComputer-science
Practice
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