Methodology {D2MCS}R Documentation

Abstract class to compute the probability prediction based on combination between metrics.

Description

Abstract class used as a template to define new customized strategies to combine the probability predictions made by different metrics.

Methods

Public methods


Method new()

Method for initializing the object arguments during runtime.

Usage
Methodology$new(required.metrics)
Arguments
required.metrics

A character vector of length greater than 2 with the name of the required metrics.


Method getRequiredMetrics()

The function returns the required metrics that will participate in the methodology to compute a metric based on all of them.

Usage
Methodology$getRequiredMetrics()
Returns

A character vector of length greater than 2 with the name of the required metrics.


Method compute()

Function to compute the probability of the final prediction based on different metrics.

Usage
Methodology$compute(raw.pred, prob.pred, positive.class, negative.class)
Arguments
raw.pred

A character list of length greater than 2 with the class value of the predictions made by the metrics.

prob.pred

A numeric list of length greater than 2 with the probability of the predictions made by the metrics.

positive.class

A character with the value of the positive class.

negative.class

A character with the value of the negative class.

Returns

A numeric value indicating the probability of the instance is predicted as positive class.


Method clone()

The objects of this class are cloneable with this method.

Usage
Methodology$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

ProbBasedMethodology


[Package D2MCS version 1.0.1 Index]