Understanding Multi-Label classification model and accuracy metrics

Author(s): Avishek Nag

Originally published on Towards AI.

The theory behind the multi-label/multi-tagging model, different umbrella classification schemes and accuracy metric analysis

Classification techniques probably are the most fundamental in Machine Learning. The majority of all online ML/AI courses and curriculums start with this.

In normal classification, we have a model defined, which classifies or tags a data instance with only one class label. Definitely, in the class set, there can(will) be multiple class labels, but the classifier will choose only one(best) among those.

Now, the question is: Can a data instance be classified/tagged with multiple possible class labels from the set? How the model should be designed and how can we calculate accuracy for that model? In this article, we will discuss theoretical… Read the full blog for free on Medium.

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Published via Towards AI