AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Feature engineering is the clever engineering of data hereby exploiting the intrinsic bias of the machine learning technique to improve the performance of any machine learning model, ideally both in terms of accuracy and interpretability at the same time.
This work analyzes current benchmarking practices and introduces a novel decomposition framework that disentangles the contribution of distinct data-generating components, such as confounding, dose distribution non-uniformity, and response surface complexity, to estimator performance.
Standard supervised learning algorithms prioritize predictive performance over causal inference, making them ill-suited for conditional average dose response (CADR) estimation. Although specialized CADR estimators have been proposed to address this, the field lacks clarity on which dataset characteristics truly challen...
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