logLoss: Log loss

In malsch/occupationCoding: Supervised Learning for Occupation Coding

Log loss

Description

Calculate log loss \log_2 loss = \frac{1}{N} \sum_n \log_2 loss_n and standard error \sqrt{\frac{1}{N(N-1)} \sum_n (\log_2 loss_n - \log_2 loss)^2} with loss_n = \sum_k -y_{nk} \log_2 p_{nk}

Usage

logLoss(occupationalPredictions)

Arguments

occupationalPredictions

a data.table created with a expandPredictionResults-function from this package.

Details

log loss is the average probability of true categories that actually realized.

Value

a data.table

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")

logLoss(res.proc)

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