MCC: Computes the Matthews correlation coefficient.

In D2MCS: Data Driving Multiple Classifier System

Computes the Matthews correlation coefficient.

Description

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.

Details

MCC = (TP × (TN - FP) × FN)/(√{(TP + FP) × (TP + FN) × (TN + FP) × (TN + FN)})

Super class

D2MCS::MeasureFunction -> MCC

Methods

Public methods

• MCC$new()

• MCC$compute()

• MCC$clone()

---

Method new()

Method for initializing the object arguments during runtime.

Usage

MCC$new(performance.output = NULL)

Arguments

performance.output

An optional ConfMatrix parameter used as basis to compute the MCC measure.

---

Method compute()

The function computes the MCC achieved by the M.L. model.

Usage

MCC$compute(performance.output = NULL)

Arguments

performance.output

An optional ConfMatrix parameter to define the type of object used as basis to compute the MCC measure.

Details

This function is automatically invoke by the ClassificationOutput object.

Returns

A numeric vector of size 1 or NULL if an error occurred.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MCC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

MeasureFunction, ClassificationOutput, ConfMatrix

D2MCS documentation built on Aug. 23, 2022, 5:07 p.m.