Topic: Adjusted Target Encoding for Multi-Class Classifications

How category-encoders library gives incorrect results for multi-class categories.

http://contrib.scikit-learn.org/category_encoders/targetencoder.html

Topic: Focal Loss for Multi-Class Classification

Focal Loss: Designed to address the class imbalance by down-weighting the easy examples even if their number is large.

https://doi.org/10.48550/arXiv.1708.02002

Take-Away Notes for Machine Learning by Stanford University on Coursera.

Week 5, Lecture 9

Take-Away Notes for Machine Learning by Stanford University on Coursera.

Week 4, Lecture 8

Take-Away Notes for Machine Learning by Stanford University on Coursera.

Week 3, Lecture 6-7

Take-Away Notes for Machine Learning by Stanford University on Coursera.

Week 2, Lecture 4-5

Take-Away Notes for Machine Learning by Stanford University on Coursera.

Week 1, Lecture 1-3

Osojnik, A., Panov, P. & Džeroski, S. Multi-label classification via multi-target regression on data streams. Mach Learn 106, 745–770 (2017). https://doi.org/10.1007/s10994-016-5613-5

Driven by FURP(FoSE Undergraduate Research Placement) Programme.

Petković, M., Kocev, D. & Džeroski, S. Feature ranking for multi-target regression. Mach Learn 109, 1179–1204 (2020). https://doi.org/10.1007/s10994-019-05829-8cement) Programme.

Waegeman, W., Dembczyński, K. & Hüllermeier, E. Multi-target prediction: a unifying view on problems and methods. Data Min Knowl Disc 33, 293–324 (2019). https://doi.org/10.1007/s10618-018-0595-5

Driven by FURP(FoSE Undergraduate Research Placement) Programme.