MinimizeFP: Combined metric strategy to minimize FP errors.

MinimizeFPR Documentation

Combined metric strategy to minimize FP errors.

Description

Calculates if the positive class is the predicted one in all metrics, otherwise, the instance is not considered to have the positive class associated.

Super class

D2MCS::CombinedMetrics -> MinimizeFP

Methods

Public methods

Inherited methods

Method new()

Method for initializing the object arguments during runtime.

Usage
MinimizeFP$new(required.metrics = c("MCC", "PPV"))
Arguments
required.metrics

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


Method getFinalPrediction()

Function to obtain the final prediction based on different metrics.

Usage
MinimizeFP$getFinalPrediction(
  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 logical value indicating if the instance is predicted as positive class or not.


Method clone()

The objects of this class are cloneable with this method.

Usage
MinimizeFP$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

CombinedMetrics


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