plotTruePredictionsVsFalsePredictions: Plot true predictions versus false predictions

In malsch/occupationCoding: Supervised Learning for Occupation Coding

Plot true predictions versus false predictions

Description

Show how many predictions would be correct as a function of how many would be incorrect.

Usage

plotTruePredictionsVsFalsePredictions(
  occupationalPredictionsAmongTopK,
  filename = NULL
)

Arguments

occupationalPredictionsAmongTopK	a data table created with calcAccurateAmongTopK.
filename	If a filename is specified the diagram will be saved at with this name.

Value

a ggplot

See Also

plotAgreementRateVsProductionRate, calcAccurateAmongTopK

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")

calcAccurateAmongTopK(res.proc, k = 5)[,mean(acc), by = method.name]
plotTruePredictionsVsFalsePredictions(calcAccurateAmongTopK(res.proc, k = 5))
plotTruePredictionsVsFalsePredictions(calcAccurateAmongTopK(res.proc, k = 1), filename = "test.pdf")

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