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Found 5 papers, 1 papers with code
On Leakage in Machine Learning Pipelines
Machine learning (ML) provides powerful tools for predictive modeling.
Confound-leakage: Confound Removal in Machine Learning Leads to Leakage
Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine.
Predictive Data Calibration for Linear Correlation Significance Testing
Inferring linear relationships lies at the heart of many empirical investigations.
Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies
Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and healthy controls.
Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression
We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies.
