Which evaluation metric is best for a multi-class classification problem?

Which evaluation metric is best for a multi-class classification problem?

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.

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