Systematic misestimation of machine learning performance in neuroimaging studies of depression
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. Here, we systematically investigated this effect focusing …