This PlatypusML_classification function takes as input encoded features obtained using the PlatypusML_extract_features function. The function runs cross validation on a specified number of folds for different classification models and reports the AUC scores and ROC curves.

This PlatypusML_classification function takes as input encoded features obtained using the PlatypusML_extract_features function. The function runs cross validation on a specified number of folds for different classification models and reports the AUC scores and ROC curves.

PlatypusML_classification(features, cv.folds, balancing, proportion)

Arguments

features

Matrix. Output of the PlatypusML_extract_features function, containing the desired label in the last column.

cv.folds

Integer. The number of folds to be used in cross validation

balancing

Boolean. Whether to perform class balancing. Defaults to TRUE.

proportion.

Vector of size 2 (floats between 0 and 1 that need to sum up to 1). Specifies the proportions for the two classes. The smaller proportion will be assigned to the minority class by default. Defaults to c(0.5,0.5).

Value

This function returns a list containing [["combined"]] summary plot with ROC & confusion matrices, [["ROC"]] the ROC curve, [["confusion"]] confusion matrices for each classifier. #' @examples