Paper in link: https://ieeexplore.ieee.org/abstract/document/9362840
A comparison of 4 boosting algorithm on various datasets
The inspiration for categorical dataset was taken from https://www.kaggle.com/asgharm1999/pokemon-classification/execution
The inspiration for image dataset was taken from https://setscholars.net/2020/03/30/image-classification-using-catboost-an-example-in-python-using-cifar10-dataset/
The inspiration of numeric dataset was taken from https://towardsdatascience.com/become-a-pok%C3%A9mon-master-with-machine-learning-f61686542ef1
The inspiration of temporal dataset was taken from https://www.kaggle.com/paulbrabban/daily-minimum-temperatures-in-melbourne
For more details about SnapBoost visit https://ibmsoe.github.io/snap-ml-doc/v1.6.0/manual.html#snap-boost.
This project was conducted as a part of IBM Remote Mentorship program under the guidance of Mr. Sangeeth Keeriyadath(Mentor,Part of IBM Watson distributed Machine Learning Frameworks) and Prof. Abhinandan S.P.(Mentor,Professor and at The National Institute Of Engineering,Mysore).
Contributors:
Shyam R(Numerical Dataset)
Anubhav Singh(Categorical Dataset)
Ayachit Sai Sanjay(Image Dataset)
Vinayak Patil(Temporal Dataset)