Threshold for multiclass classification

1. How do you determine the threshold for multiclass classification?
2. What is a good accuracy for multiclass classification?
3. What is a good metric for multiclass classification?
4. What is threshold value in classification?

How do you determine the threshold for multiclass classification?

Unlike the process for binary classification problems, you do not need to choose a score threshold to make predictions. The predicted answer is the class (i.e., label) with the highest predicted score.

What is a good accuracy for multiclass classification?

The prevailing metrics for evaluating a multiclass classification model are: Accuracy: The proportion of predictions that were correct. It is generally converted to a percentage where 100% is a perfect classifier. For a balanced dataset, an accuracy of 100%k where k is the number of classes, is a random classifier.

What is a good metric for multiclass classification?

Most commonly used metrics for multi-classes are F1 score, Average Accuracy, Log-loss.

What is threshold value in classification?

What Is the Classification Threshold? The classification threshold in ML, also called the decision threshold, allows us to map the sigmoid output of a binary classification to a binary category. Let's take an example of logistic regression applied to spam detection, where the two classes are spam and non-spam.