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Found 4 papers, 2 papers with code
Validity problems in clinical machine learning by indirect data labeling using consensus definitions
We demonstrate a validity problem of machine learning in the vital application area of disease diagnosis in medicine.
Towards Inferential Reproducibility of Machine Learning Research
Instead of removing noise, we propose to incorporate several sources of variance, including their interaction with data properties, into an analysis of significance and reliability of machine learning evaluation, with the aim to draw inferences beyond particular instances of trained models.
Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis
Ensembling neural networks is a long-standing technique for improving the generalization error of neural networks by combining networks with orthogonal properties via a committee decision.
False perfection in machine prediction: Detecting and assessing circularity problems in machine learning
This paper is an excerpt of an early version of Chapter 2 of the book "Validity, Reliability, and Significance.
