feature selection on each fold of a jackknife for three additional datasets from the UCI Machine Learning Repository with a similar shape. First, the Data for Software Engineering Teamwork Assessment in Education Setting Data Set (SETAP) [@setap] has 74 observations of 84 predictors. Then, the Urban Land Cover Data Set (ULC) [@ulc] has 168 observations of 147 predictors. Lastly, the Arrhythmia Data Set (ARR) [@arr] has 452 observations of 279 predictors. Since these datasets are being used for computational benchmarking but not predictive analysis, their predictors and features will not be discussed.

Predictive Models
-----------------

To demonstrate the efficacy of the DC-SIS method, we will be competing against the “All feature subsets” section of Table 5 from [@compare]. In this portion of the table, Sakar et al. demonstrated the accuracy, F1 score, and Matthews correlation coefficient (MCC) of Naive Bayes, Logistic Regression, k-Nearest Neighbor, Multilayer Perceptron, Random Forest, and Support Vector Classifiers with linear and radial kernels with features selected from all of those available.

We will now describe the settings used for each machine learning model. Whenever possible, a grid search was used to optimize the hyperparamters. Since Naïve Bayes has no hyperparameters other than the prior probability of 