classificationErrors: Calculating classification errors and other prediction...
In genderizeR: Gender Prediction Based on First Names

Description Usage Arguments Value Examples

classificationErrors builds confusion matrix from manually coded and predicted gender vectors and returns classification errors calculated on that matrix.

1	classificationErrors(labels, predictions)

`labels`	A vector of true labels. Should have following values: c("female", "male", "unknown", "noname"). `noname` stands also for initials only.
`predictions`	A vector of predicted gender. Should have following values: c("female", "male", NA). `NA` when it was not possible to predict a gender.

A list of gender prediction efficiency indicators:

confMatrix: Full confusion matrix.
errorTotal: Total classification error calculated on the matrix.
errorFullFirstNames: Classification error calculated without "noname" category.
errorCoded: Classification error calculated without both "noname" and "unknown" category.
errorCodedWithoutNA: Classification error calculated only on "female" and "male" categories from both predictions and labels.
naTotal: Total proportion of items with unpredicted gender.
naFullFirstNames: Proportion of items with unpredicted gender calculated without "noname" category.
naCoded: Proportion of items with unpredicted gender calculated without both "noname" and "unknown" category.
errorGenderBias: Calculated as follows: "male" classified as "female" minus "female" classified as "male" and divided by the sum of items in "female" and "male" categories from both predictions and labels.

suppressWarnings(RNGversion("3.5.0"))
set.seed(23)
labels = sample(c("female", "male", "unknown", "noname"), 100, replace = TRUE)
predictions = sample(c("female", "male", NA), 100, replace = TRUE)
classificationErrors(labels, predictions)

# $confMatrix
#          predictions
# labels    female male <NA>
#   female       6    6    8
#   male         6   10   10
#   noname      12    6   17
#   unknown      5    7    7
#   <NA>         0    0    0
# 
# $errorTotal
# [1] 0.67
# 
# $errorFullFirstNames
# [1] 0.6461538
# 
# $errorCoded
# [1] 0.6521739
# 
# $errorCodedWithoutNA
# [1] 0.4285714
# 
# $naTotal
# [1] 0.42
# 
# $naFullFirstNames
# [1] 0.3846154
# 
# $naCoded
# [1] 0.3913043
# 
# $errorGenderBias
# [1] 0