Introduction to correctR

Trent Henderson




correctR is a lightweight package that implements a small number of corrected test statistics for cases when samples of two machine learning model metrics (e.g., classification accuracy) are not independent (and therefore are correlated), such as in the case of resampling and \(k\)-fold cross-validation. We demonstrate the basic functionality here using some trivial examples for the following corrected tests that are currently implemented in correctR:

These corrections were all originally proposed by Nadeau and Bengio (2003)1 with additional representations in Bouckaert and Frank (2004)2.

Random subsampling correction

In random subsampling, the standard \(t\)-test inflates Type I error when used in conjunction with random subsampling due to an underestimation of the variance, as found by Dietterich (1998)3. Nadeau and Bengio (2003) proposed a solution (which we implement as resampled_ttest in correctR) in the form of:

\[ t = \frac{\frac{1}{n} \sum_{j=1}^{n}x_{j}}{\sqrt{(\frac{1}{n} + \frac{n_{2}}{n_{1}})\sigma^{2}}} \]

where \(n\) is the number of resamples (NOTE: \(n\) is not sample size), \(n_{1}\) is the number of samples in the training data, and \(n_{2}\) is the number of samples in the test data. \(\sigma^{2}\) is the variance estimate used in the standard paired \(t\)-test (which simply has \(\frac{\sigma}{\sqrt{n}}\) in the denominator where \(n\) is the sample size in this case).

k-fold cross-validation correction

There is an alternate formulation of the random subsampling correction, devised in terms of the unbiased estimator \(\rho\), discussed in Corani et al. (2016)4 which we implement as kfold_tttest in correctR:

\[ t = \frac{\frac{1}{n} \sum_{j=1}^{n}x_{j}}{\sqrt{(\frac{1}{n} + \frac{\rho}{1-\rho})\sigma^{2}}} \]

where \(n\) is the number of resamples and \(\rho = \frac{1}{k}\) where \(k\) is the number of folds in the \(k\)-fold cross-validation procedure. This formulation stems from the fact that Nadeau and Bengio (2003) proved there is no unbiased estimator, but it can be approximated with \(\rho = \frac{1}{k}\).

Repeated k-fold cross-validation correction

Repeated \(k\)-fold cross-validation is more complex than the previous case(s) as we now have \(r\) repeats for every fold \(k\). Bouckaert and Frank (2004) present a nice representation of the corrected test for this case which we implement as repkfold_ttest in correctR:

\[ t = \frac{\frac{1}{k \cdot r} \sum_{i=1}^{k} \sum_{j=1}^{r} x_{ij}}{\sqrt{(\frac{1}{k \cdot r} + \frac{n_{2}}{n_{1}})\sigma^{2}}} \]


In the real world, we would have proper results obtained through fitting two models according to one or more of the procedures outlined above. For simplicity here, we are just going to simulate three datasets so we can get to the package functionality cleaner and easier. We are going to assume we are in a classification context and generate classification accuracy values. These values are purposefully egregious—we are going to (in the case of the random subsampling) just fix the train set sample size (n1) to 80 and the test set sample size (n2) to 20, and assume (using the same data) for the \(k\)-fold cross-validation correction that the same numbers were obtained on such a method. Again, the values are not important here, it is the interface for performing corrections that is the focus.

In the case of repeated \(k\)-fold cross-validation, take note of the column names. While your data.frame you pass in to repkfold_ttest can have more than the four columns specified here, it must contain at least these four with the exact corresponding names. The function explicitly searches for them. They are:

  1. "model" — contains a label for each of the two models to compare
  2. "values" — the numerical values of the performance metric (i.e., classification accuracy)
  3. "k" — which fold the values correspond to
  4. "r" — which repeat of the fold the values correspond to
set.seed(123) # For reproducibility

# Data for random subsampling and k-fold cross-validation corrections

x <- stats::rnorm(30, mean = 0.6, sd = 0.1)
y <- stats::rnorm(30, mean = 0.4, sd = 0.1)

# Data for repeated k-fold cross-validation correction

tmp <- data.frame(model = rep(c(1, 2), each = 60),
                  values = c(stats::rnorm(60, mean = 0.6, sd = 0.1), 
                             stats::rnorm(60, mean = 0.4, sd = 0.1)),
                  k = rep(c(1, 1, 2, 2), times = 15),
                  r = rep(c(1, 2), times = 30))

Package functionality

We can fit all the corrections in one-line functions:

rss <- resampled_ttest(x = x, y = y, n = 30, n1 = 80, n2 = 20)
kcv <- kfold_ttest(x = x, y = y, n = 100, k = 30)
rkcv <- repkfold_ttest(data = tmp, n1 = 80, n2 = 20, k = 2, r = 2)

All the functions return a data.frame with two named columns: "statistic" (the \(t\)-statistic) and "p.value" (the associated \(p\)-value), meaning they can be easily integrated into complex machine pipelines. Here is an example for resampled_ttest:

##   statistic    p.value
## 1  2.407318 0.02265982

Note that all three functions express the hypothesis test as a two-tailed test by default. If we wanted to specify a one-tailed (directional) hypothesis, we can set tailed = "one" in any of the functions. Note that if we do so, we must specify the direction. In the case of resampled_ttest and kfold_ttest, this is as simple as setting greater = "x" if we expect \(x > y\), or greater = "y" if we expect \(y > x\), such as:

resampled_ttest(x = x, y = y, n = 30, n1 = 80, n2 = 20, 
                tailed = "one", greater = "x")
##   statistic    p.value
## 1  2.407318 0.01132991
kfold_ttest(x = x, y = y, n = 100, k = 30, 
            tailed = "one", greater = "x")
##   statistic      p.value
## 1  6.052149 1.281991e-08

In the case of repkfold_ttest, since we have a data frame, we need to pass in the value in the model column which corresponds to the model we expect to demonstrate greater values, such as:

repkfold_ttest(data = tmp, n1 = 80, n2 = 20, k = 2, r = 2, 
               tailed = "one", greater = 1)
##   statistic    p.value
## 1   1.97102 0.07165217

  1. Nadeau, C., and Bengio, Y. Inference for the Generalization Error. Machine Learning, 52, 239-281, (2003).↩︎

  2. Bouckaert, R. R., and Frank, E. Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science, 3056, (2004).↩︎

  3. Dietterich, T. G. (1998). Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation, 10(7)↩︎

  4. Corani, G., Benavoli, A., Demsar, J., Mangili, F., and Zaffalon, M. Statistical comparison of classifiers through Bayesian hierarchical modelling. Machine Learning, 106, (2017).↩︎