The yvsoucom-iterkit framework demonstrates that healthcare risk prediction pipelines have a structured search space dominated by a small set of high-impact components such as augmentation and imbalance handling, with substantial redundancy among variants.
Efficient and robust automated machine learning.Advances in neural information processing systems, 28, 2015
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction
The yvsoucom-iterkit framework demonstrates that healthcare risk prediction pipelines have a structured search space dominated by a small set of high-impact components such as augmentation and imbalance handling, with substantial redundancy among variants.