To properly evaluate a machine learning model, the available data must be split into training and test subsets. Data leakage occurs when, in one way or another, information regarding the test set inappropriately influences the training or evaluation of the model. This causes us to overestimated the performance of a ... Read more ...

P-values are currently the most common metric used to draw conclusions from research. Given this, one would expect they are well-understood and non-controversial. Nothing could be further from the truth. Recently, misconceptions about their use and misinterpretation of their meaning have even triggered a statement from the American Statistical Association ... Read more ...

Recently a landmark study[1] was published that claimed to have identified a danger of "black box" machine learning methods. The main message of the article is that there is a trade-off between intelligibility and accuracy. They write that, while models like neural networks often more accurate, it is difficult ... Read more ...