Which evaluation metric is most appropriate for a multi-class classification problem?
Practice Questions
1 question
Q1
Which evaluation metric is most appropriate for a multi-class classification problem?
Accuracy
F1 Score
Log Loss
All of the above
All of these metrics can be used to evaluate multi-class classification problems, depending on the specific context and requirements.
Questions & Step-by-step Solutions
1 item
Q
Q: Which evaluation metric is most appropriate for a multi-class classification problem?
Solution: All of these metrics can be used to evaluate multi-class classification problems, depending on the specific context and requirements.
Steps: 5
Step 1: Understand what a multi-class classification problem is. This is when you have more than two classes to predict.
Step 2: Learn about different evaluation metrics that can be used for multi-class classification, such as accuracy, precision, recall, and F1 score.
Step 3: Recognize that the choice of metric depends on the specific goals of your classification task. For example, if you care more about correctly identifying a certain class, you might prioritize precision or recall for that class.
Step 4: Consider the context of your problem. If you have imbalanced classes, some metrics like F1 score might be more informative than accuracy.
Step 5: Conclude that all these metrics can be useful, and the best one to use will depend on your specific needs and the characteristics of your data.
