Briefly, gene subset selection was performed by CFS method, using K-fold error estimation (10 repetitions, 5-fold default parameters) and Random Forest algorithm.
However, if investagtors whish to report a testing result in the training cohort, an alternative, though less optimal, study design is using k-fold method or split the training cohort data randomly (2-fold), with 50% of the data being assigned to develop the score, compound covariate, and 50% to evaluate its performance.
Results from Finite Element Method (FEM) model are used to train and test the developed Fuzzy Support Vector Regression model tuned with Genetic Algorithm (FSVRGA) using K-fold cross validation method.
This study used k-fold cross validation method to partition data into training and test sets where k = 10 is used.
Then, models were constructed using supervised learning algorithms: C4.5, NB Tree and MLP for diagnosis of CAD and its severity The models are trained and validated using k-fold cross-validation method, where all the samples are eventually used for both training and testing.
In addition, to assess the internal validity of our model, we used k-fold cross-validation method using the entire dataset as both for development and validation of the model [ 19].
To test the accuracy of classification models, we use k-fold cross validation, which is a common method for estimating the error of a model on benchmark medical data sets.
Experiments were conducted using k-fold cross-validation strategy with a k value of 10.
Experiments were conducted using k-fold cross validation, with a k value of 10.
Using k-fold testing provides a more-in-detail judgment of the models.
