The Matthews correlation coefficient is used in machine learning as a measure of the quality of binary (two-class) classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The MCC is in essence a correlation coefficient between the observed and predicted binary classifications; it returns a value between -1 and +1.
D2MCS::MeasureFunction -> MCC
new()Method for initializing the object arguments during runtime.
MCC$new(performance.output = NULL)performance.outputAn optional ConfMatrix parameter
used as basis to compute the MCC measure.
compute()The function computes the MCC achieved by the M.L. model.
MCC$compute(performance.output = NULL)performance.outputAn optional ConfMatrix parameter
to define the type of object used as basis to compute the MCC
measure.
This function is automatically invoke by the ClassificationOutput object.
clone()The objects of this class are cloneable with this method.
MCC$clone(deep = FALSE)deepWhether to make a deep clone.
$$MCC = (TP × (TN - FP) × FN)/(\sqrt{(TP + FP) × (TP + FN) × (TN + FP) × (TN + FN)})$$
MeasureFunction, ClassificationOutput,
ConfMatrix