ser: Non-Standard Evaluation Metrics

In nunompmoniz/IRon: Solving Imbalanced Regression Tasks

Non-Standard Evaluation Metrics

Description

Obtains the squared error of predictions for a given subset of relevance

Usage

ser(trues, preds, phi.trues = NULL, ph = NULL, t = 0)

Arguments

trues	Target values from a test set of a given data set. Should be a vector and have the same size as the variable preds
preds	Predicted values given a certain test set of a given data set. Should be a vector and have the same size as the variable preds
phi.trues	Relevance of the values in the parameter trues. Use ??phi() for more information. Defaults to NULL
ph	The relevance function providing the data points where the pairs of values-relevance are known. Default is NULL
t	Relevance cut-off. Default is 0.

Details

Squared Error-Relevance Metric (SER)

Value

Squared error for for cases where the relevance of the true value is greater than t (SERA)

Examples

library(IRon)
library(rpart)

if(requireNamespace("rpart")) {

   data(accel)

   form <- acceleration ~ .

   ind <- sample(1:nrow(accel),0.75*nrow(accel))

   train <- accel[ind,]
   test <- accel[-ind,]

   ph <- phi.control(accel$acceleration)

   m <- rpart::rpart(form, train)
   preds <- as.vector(predict(m,test))

   trues <- test$acceleration
   phi.trues <- phi(test$acceleration,ph)

   ser(trues,preds,phi.trues)

}

nunompmoniz/IRon documentation built on April 24, 2023, 1:20 p.m.