plotReliabilityDiagram: Reliability Diagram

In malsch/occupationCoding: Supervised Learning for Occupation Coding

Reliability Diagram

Description

Plots the observed relative frequency of correctness against the forecasted probability.

Usage

plotReliabilityDiagram(occupationalPredictions, k, num.codes, filename = NULL)

Arguments

k	how many top k categories to aggregate over?
num.codes	Number of allowed categories in classification
filename	If a filename is specified the diagram will be saved at with this name.
occupationalPredictionsAmongTopK	a data table created with calcAccurateAmongTopK.

Value

a ggplot

See Also

sharpness

Examples

# set up data
data(occupations)
allowed.codes <- c("71402", "71403", "63302", "83112", "83124", "83131", "83132", "83193", "83194", "-0004", "-0030")
allowed.codes.titles <- c("Office clerks and secretaries (without specialisation)-skilled tasks", "Office clerks and secretaries (without specialisation)-complex tasks", "Gastronomy occupations (without specialisation)-skilled tasks",
 "Occupations in child care and child-rearing-skilled tasks", "Occupations in social work and social pedagogics-highly complex tasks", "Pedagogic specialists in social care work and special needs education-unskilled/semiskilled tasks", "Pedagogic specialists in social care work and special needs education-skilled tasks", "Supervisors in education and social work, and of pedagogic specialists in social care work", "Managers in education and social work, and of pedagogic specialists in social care work",
 "Not precise enough for coding", "Student assistants")
proc.occupations <- removeFaultyAndUncodableAnswers_And_PrepareForAnalysis(occupations, colNames = c("orig_answer", "orig_code"), allowed.codes, allowed.codes.titles)

## split sample
set.seed(3451345)
n.test <- 50
group <- sample(c(rep("test", n.test), rep("training", nrow(proc.occupations) - n.test)))
splitted.data <- split(proc.occupations, group)

# train model and make predictions
model <- trainLogisticRegressionWithPenalization(splitted.data$train, preprocessing = list(stopwords = tm::stopwords("de"), stemming = "de", countWords = FALSE), tuning = list(alpha = 0.05, maxit = 50^5, nlambda = 100, thresh = 1e-5))
res <- predictLogisticRegressionWithPenalization(model, splitted.data$test)

# expand to contain more categories than the initial ones
res.proc1 <- expandPredictionResults(res, allowed.codes = c("12345", allowed.codes), method.name = "glmnet1")

# we can use different methods to create a combined dataset. This is how to run the subsequent analysis functions only once.
res.proc2 <- expandPredictionResults(res, allowed.codes = c("12345", allowed.codes), method.name = "glmnet2")
res.proc <- rbind(res.proc1, res.proc2); class(res.proc) <- c(class(res.proc), "occupationalPredictionsComplete")

plotReliabilityDiagram(res.proc, k = 5, num.codes = length(allowed.codes) + 1) # + 1 because we introduced the code "12345" later
plotReliabilityDiagram(res.proc, k = 1, num.codes = length(allowed.codes) + 1, filename = "test.pdf")

malsch/occupationCoding documentation built on March 14, 2024, 8:09 a.m.