Which evaluation metric is best for a multi-class classification problem?
Practice Questions
1 question
Q1
Which evaluation metric is best for a multi-class classification problem?
Accuracy
F1 Score
Log Loss
All of the above
All of the mentioned metrics (Accuracy, F1 Score, and Log Loss) can be used to evaluate multi-class classification problems, each providing different insights into model performance.
Questions & Step-by-step Solutions
1 item
Q
Q: Which evaluation metric is best for a multi-class classification problem?
Solution: All of the mentioned metrics (Accuracy, F1 Score, and Log Loss) can be used to evaluate multi-class classification problems, each providing different insights into model performance.
Steps: 6
Step 1: Understand that a multi-class classification problem involves predicting one out of several classes (more than two).
Step 2: Learn about different evaluation metrics used for assessing model performance.
Step 3: Recognize that Accuracy measures the overall correctness of the model's predictions.
Step 4: Understand that F1 Score balances precision and recall, which is useful when classes are imbalanced.
Step 5: Know that Log Loss evaluates the probabilities assigned to each class, penalizing incorrect predictions more heavily.
Step 6: Realize that each metric provides different insights, so it's important to consider the context of your problem when choosing one.
