MinimizeFN: Combined metric strategy to minimize FN errors.

In D2MCS: Data Driving Multiple Classifier System

Combined metric strategy to minimize FN errors.

Description

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

Super class

D2MCS::CombinedMetrics -> MinimizeFN

Methods

Public methods

• MinimizeFN$new()

• MinimizeFN$getFinalPrediction()

• MinimizeFN$clone()

Inherited methods

• D2MCS::CombinedMetrics$getRequiredMetrics()

---

Method new()

Method for initializing the object arguments during runtime.

Usage

MinimizeFN$new(required.metrics = c("MCC", "PPV"))

Arguments

required.metrics

A character vector of length 1 with the name of the required metrics.

---

Method getFinalPrediction()

Function to obtain the final prediction based on different metrics.

Usage

MinimizeFN$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

MinimizeFN$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.