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Corrected test statistics for comparing machine learning models on correlated samples
You can install the stable version of correctR from CRAN:
You can install the development version of correctR from GitHub:
Often in machine learning, we want to compare the performance of different models to determine if one statistically outperforms another. However, the methods used (e.g., data resampling, \(k\)-fold cross-validation) to obtain these performance metrics (e.g., classification accuracy) violate the assumptions of traditional statistical tests such as a \(t\)-test. The purpose of these methods is to either aid generalisability of findings (i.e., through quantification of error as they produce multiple values for each model instead of just one) or to optimise model hyperparameters. This makes them invaluable, but unusable with traditional tests, as Dietterich (1998) found that the standard \(t\)-test underestimates the variance, therefore driving a high Type I error. correctR is a lightweight package that implements a small number of corrected test statistics for cases when samples are not independent (and therefore are correlated), such as in the case of resampling, \(k\)-fold cross-validation, and repeated \(k\)-fold cross-validation. These corrections were all originally proposed by Nadeau and Bengio (2003). Currently, only cases where two models are to be compared are supported.
A Python version of correctR called correctipy is available at the GitHub repository.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.