1 code implementation • 21 Jan 2022 • Yotam Elor, Hadar Averbuch-Elor
Balancing the data before training a classifier is a popular technique to address the challenges of imbalanced binary classification in tabular data.
no code implementations • 17 May 2021 • Sajad Darabi, Yotam Elor
Furthermore, the superior synthetic data yields better prediction quality in downstream binary classification tasks, as was demonstrated in extensive experiments with 27 publicly available real-world datasets
no code implementations • 1 Jan 2021 • Sajad Darabi, Yotam Elor
Real-world binary classification tasks are in many cases unbalanced i. e. the minority class is much smaller than the majority class.
no code implementations • 15 Dec 2020 • Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias Seeger, Bhaskar Dutt, Laurence Rouesnel
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.