In a multi-class classification problem, which metric can be used to evaluate the performance across all classes?

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Q: In a multi-class classification problem, which metric can be used to evaluate the performance across all classes?

Solution: Micro F1 Score aggregates the contributions of all classes to compute the average metric, suitable for multi-class evaluation.

Steps: 7

Step 1: Understand that in a multi-class classification problem, there are multiple classes to predict.
Step 2: Know that we need a way to measure how well the model is performing across all these classes.
Step 3: Learn about different metrics that can be used for evaluation, such as accuracy, precision, recall, and F1 score.
Step 4: Focus on the F1 score, which combines precision and recall into one number.
Step 5: Understand that the Micro F1 Score is a specific way to calculate the F1 score for multiple classes.
Step 6: Realize that the Micro F1 Score takes into account the total true positives, false positives, and false negatives across all classes.
Step 7: Conclude that using the Micro F1 Score is a good choice for evaluating performance in multi-class classification problems.