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Small changes of the texts in the note section #1803

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Aug 9, 2017
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Merged

Small changes of the texts in the note section #1803

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4d9eb29
Tpr: in Tpr and gmean we use the term specificity, therefore I think …
May 21, 2017
cfce72e
mmce.test is basically manual< comparing two vectors (the truth and t…
May 21, 2017
1048da9
no message
May 21, 2017
e9af7a4
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May 22, 2017
4eec732
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May 22, 2017
245d983
number of fp is the same as fp
May 24, 2017
443aa9f
Merge branch 'master' into measures_update
Jun 13, 2017
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Aug 7, 2017
8f75510
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Aug 7, 2017
33624c1
Merge branch 'master' into measures_update
Aug 7, 2017
177883b
Merge branch 'master' into measures_update
Aug 7, 2017
9165abe
Merge branch 'master' into measures_update
berndbischl Aug 9, 2017
564cf3e
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berndbischl Aug 9, 2017
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10 changes: 5 additions & 5 deletions R/measures.R
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Original file line number Diff line number Diff line change
Expand Up @@ -974,7 +974,7 @@ measureFN = function(truth, response, negative) {
tpr = makeMeasure(id = "tpr", minimize = FALSE, best = 1, worst = 0,
properties = c("classif", "req.pred", "req.truth"),
name = "True positive rate",
note = "Percentage of correctly classified observations in the positive class. Also called hit rate or recall.",
note = "Percentage of correctly classified observations in the positive class. Also called hit rate or recall or sensitivity.",
fun = function(task, model, pred, feats, extra.args) {
measureTPR(pred$data$truth, pred$data$response, pred$task.desc$positive)
}
Expand Down Expand Up @@ -1050,7 +1050,7 @@ measureFNR = function(truth, response, negative, positive) {
ppv = makeMeasure(id = "ppv", minimize = FALSE, best = 1, worst = 0,
properties = c("classif", "req.pred", "req.truth"),
name = "Positive predictive value",
note = "Defined as: tp / (tp + number of fp). Also called precision. If the denominator is 0, PPV is set to be either 1 or 0 depending on whether the highest probability prediction is positive (1) or negative (0).",
note = "Defined as: tp / (tp + fp). Also called precision. If the denominator is 0, PPV is set to be either 1 or 0 depending on whether the highest probability prediction is positive (1) or negative (0).",
fun = function(task, model, pred, feats, extra.args) {
if (pred$predict.type == "prob") {
prob = getPredictionProbabilities(pred)
Expand Down Expand Up @@ -1086,7 +1086,7 @@ measureEdgeCase = function(truth, positive, prob) {
npv = makeMeasure(id = "npv", minimize = FALSE, best = 1, worst = 0,
properties = c("classif", "req.pred", "req.truth"),
name = "Negative predictive value",
note = "Defined as: (tn) / (tn + fn).",
note = "Defined as: tn / (tn + fn).",
fun = function(task, model, pred, feats, extra.args) {
measureNPV(pred$data$truth, pred$data$response, pred$task.desc$negative)
}
Expand All @@ -1105,7 +1105,7 @@ measureNPV = function(truth, response, negative) {
fdr = makeMeasure(id = "fdr", minimize = TRUE, best = 0, worst = 1,
properties = c("classif", "req.pred", "req.truth"),
name = "False discovery rate",
note = "Defined as: (fp) / (tn + fn).",
note = "Defined as: fp / (tp + fp).",
fun = function(task, model, pred, feats, extra.args) {
measureFDR(pred$data$truth, pred$data$response, pred$task.desc$positive)
}
Expand All @@ -1124,7 +1124,7 @@ measureFDR = function(truth, response, positive) {
mcc = makeMeasure(id = "mcc", minimize = FALSE,
properties = c("classif", "req.pred", "req.truth"), best = 1, worst = -1,
name = "Matthews correlation coefficient",
note = "Defined as sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)), denominator set to 1 if 0",
note = "Defined as (tp * tn - fp * fn) / sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)), denominator set to 1 if 0",
fun = function(task, model, pred, feats, extra.args) {
measureMCC(pred$data$truth, pred$data$response, pred$task.desc$negative, pred$task.desc$positive)
}
Expand Down
2 changes: 1 addition & 1 deletion tests/testthat/test_base_measures.R
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Expand Up @@ -356,7 +356,7 @@ test_that("check measure calculations", {
#test multiclass measures

#mmce
mmce.test = mean(c(1L != 1L, 2L != 0L, 0L != 0L, 1L != 2L))
mmce.test = mean(c(1L != 1L, 2L != 1L, 0L != 0L, 1L != 2L))
mmce.perf = performance(pred.classif, measures = mmce, model = mod.classif)
expect_equal(mmce.test, mmce$fun(pred = pred.classif))
expect_equal(mmce.test, as.numeric(mmce.perf))
Expand Down