Methodology: Abstract class to compute the probability prediction based on...

In D2MCS: Data Driving Multiple Classifier System

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

• Methodology$new()

• Methodology$getRequiredMetrics()

• Methodology$compute()

• Methodology$clone()

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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.

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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.

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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.

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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

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