binary_cross_entropy: Estimates the inverse binary cross-entropy, i.e. 0 is the...

In autoMrP: Improving MrP with Ensemble Learning

Estimates the inverse binary cross-entropy, i.e. 0 is the best score and 1 the worst.

Description

binary_cross_entropy() estimates the inverse binary cross-entropy on the individual and state-level.

Usage

binary_cross_entropy(
  pred,
  data.valid,
  loss.unit = c("individuals", "L2 units"),
  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.
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 containing two binary cross-entropy prediction errors. The first is measured at the level of individuals and the second is measured at the context level. The tibble dimensions are 2x3 with variables: measure, value and level.

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