loss_function: Estimates loss value.

In autoMrP: Improving MrP with Ensemble Learning

Estimates loss value.

Description

loss_function() estimates the loss based on a loss function.

Usage

loss_function(
  pred,
  data.valid,
  loss.unit = c("individuals", "L2 units"),
  loss.fun = c("MSE", "MAE", "cross-entropy"),
  y,
  L2.unit
)

Arguments

pred	Predictions of outcome. A numeric vector of outcome predictions.
data.valid	Test data set. A tibble of data that was not used for prediction.
loss.unit	Loss function unit. A character-valued scalar indicating whether performance loss should be evaluated at the level of individual respondents (individuals) or geographic units (L2 units). Default is individuals.
loss.fun	Loss function. A character-valued scalar indicating whether prediction loss should be measured by the mean squared error (MSE) or the mean absolute error (MAE). Default is MSE.
y	Outcome variable. A character vector containing the column names of the outcome variable.
L2.unit	Geographic unit. A character scalar containing the column name of the geographic unit in survey and census at which outcomes should be aggregated.

Value

Returns a tibble with number of rows equal to the number of loss functions tested (defaults to 4 for cross-entropy, f1, MSE, and msfe). The number of columns is 2 where the first is called measure and contains the names of the loss-functions and the second is called value and contains the loss-function scores.

autoMrP documentation built on May 29, 2024, 6:40 a.m.