Week 13: 11/22/2016 - 11/29/2016

This past week I read and summarized the paper, Multi-Label Classification: An Overview, as a PowerPoint presentation. The paper explains 14 different multi-label classification algorithms (although many of the algorithms overlap or are variations of each other). This paper also experimentally compares three of the algorithms using Hamming Loss and accuracy metrics. It determined that the transformation algorithms PT3 and PT4 predicts the best with accuracy and Hamming Loss metrics respectively. I learned what multi-label classification is compared to single-label classification. Multi-label classification assigns multiple prediction labels to a single data sample instead of a single prediction label to a single data sample. Multi-label classification makes the most sense for the Stack Overflow tag prediction analysis we will be performing next semester, because we want to label a question with multiple tags ("prediction labels").