From 19eddd80925d6e97caf9ee4a67eac1658d074573 Mon Sep 17 00:00:00 2001 From: Michel Lang Date: Wed, 19 Jan 2022 16:48:23 +0100 Subject: [PATCH] release: 0.13.1 (#758) --- DESCRIPTION | 4 ++-- NEWS.md | 12 +++++++++++- R/BenchmarkResult.R | 4 ++-- R/LearnerClassifFeatureless.R | 2 +- R/LearnerRegrFeatureless.R | 2 +- R/worker.R | 4 +++- man/BenchmarkResult.Rd | 4 ++-- man/mlr_learners_classif.featureless.Rd | 2 +- man/mlr_learners_regr.featureless.Rd | 2 +- man/mlr_measures_classif.acc.Rd | 7 ++++++- man/mlr_measures_classif.auc.Rd | 8 ++++++-- man/mlr_measures_classif.bacc.Rd | 7 ++++++- man/mlr_measures_classif.bbrier.Rd | 7 ++++++- man/mlr_measures_classif.ce.Rd | 7 ++++++- man/mlr_measures_classif.dor.Rd | 10 +++++++--- man/mlr_measures_classif.fbeta.Rd | 17 ++++++++--------- man/mlr_measures_classif.fdr.Rd | 10 +++++++--- man/mlr_measures_classif.fn.Rd | 7 ++++++- man/mlr_measures_classif.fnr.Rd | 10 +++++++--- man/mlr_measures_classif.fomr.Rd | 10 +++++++--- man/mlr_measures_classif.fp.Rd | 7 ++++++- man/mlr_measures_classif.fpr.Rd | 10 +++++++--- man/mlr_measures_classif.logloss.Rd | 7 ++++++- man/mlr_measures_classif.mbrier.Rd | 5 +++++ man/mlr_measures_classif.mcc.Rd | 10 +++++++--- man/mlr_measures_classif.npv.Rd | 10 +++++++--- man/mlr_measures_classif.ppv.Rd | 10 +++++++--- man/mlr_measures_classif.prauc.Rd | 8 ++++++-- man/mlr_measures_classif.precision.Rd | 10 +++++++--- man/mlr_measures_classif.recall.Rd | 10 +++++++--- man/mlr_measures_classif.sensitivity.Rd | 10 +++++++--- man/mlr_measures_classif.specificity.Rd | 10 +++++++--- man/mlr_measures_classif.tn.Rd | 7 ++++++- man/mlr_measures_classif.tnr.Rd | 10 +++++++--- man/mlr_measures_classif.tp.Rd | 7 ++++++- man/mlr_measures_classif.tpr.Rd | 10 +++++++--- man/mlr_measures_regr.bias.Rd | 5 ++++- man/mlr_measures_regr.ktau.Rd | 5 ++++- man/mlr_measures_regr.mae.Rd | 5 ++++- man/mlr_measures_regr.mape.Rd | 8 +++++--- man/mlr_measures_regr.maxae.Rd | 5 ++++- man/mlr_measures_regr.medae.Rd | 5 ++++- man/mlr_measures_regr.medse.Rd | 5 ++++- man/mlr_measures_regr.mse.Rd | 5 ++++- man/mlr_measures_regr.msle.Rd | 8 +++++--- man/mlr_measures_regr.pbias.Rd | 5 ++++- man/mlr_measures_regr.rae.Rd | 8 +++++--- man/mlr_measures_regr.rmse.Rd | 5 ++++- man/mlr_measures_regr.rmsle.Rd | 8 +++++--- man/mlr_measures_regr.rrse.Rd | 8 +++++--- man/mlr_measures_regr.rse.Rd | 8 +++++--- man/mlr_measures_regr.rsq.Rd | 8 +++++--- man/mlr_measures_regr.sae.Rd | 5 ++++- man/mlr_measures_regr.smape.Rd | 8 +++++--- man/mlr_measures_regr.srho.Rd | 5 ++++- man/mlr_measures_regr.sse.Rd | 5 ++++- man/mlr_measures_sim.jaccard.Rd | 7 ++++--- man/mlr_measures_sim.phi.Rd | 7 ++++--- 58 files changed, 297 insertions(+), 118 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 6d2f92b7e..3f13eaf48 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: mlr3 Title: Machine Learning in R - Next Generation -Version: 0.13.0-9000 +Version: 0.13.1 Authors@R: c(person(given = "Michel", family = "Lang", @@ -67,7 +67,7 @@ Imports: future.apply (>= 1.5.0), lgr (>= 0.3.4), mlbench, - mlr3measures (>= 0.4.0), + mlr3measures (>= 0.4.1), mlr3misc (>= 0.10.0), parallelly, palmerpenguins, diff --git a/NEWS.md b/NEWS.md index 89fc8f86c..daf52537d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,13 @@ +# mlr3 0.13.1 + +* Improved performance for many operations on `ResampleResult` and + `BenchmarkResult`. +* `resample()` and `benchmark()` got a new argument `clone` to control which + objects to clone before performing computations. +* Tasks are checked for infinite values during the conversion from `data.frame` + to `Task` in `as_task_classif()` and `as_task_regr()`. A warning is signaled + if any column contains infinite values. + # mlr3 0.13.0 * Learners which are capable of resuming/continuing (e.g., @@ -87,7 +97,7 @@ respectively. Note that this behavior will eventually will be the default for future releases. * Prediction objects generated by `Learner$predict_newdata()` now have row ids - starting from 1 instead auto incrementing row ids of the training task. + starting from 1 instead auto incremented row ids of the training task. * `as.data.table.DictionaryTasks` now returns an additional column `properties`. * Added flag `conditions` to `ResampleResult$score()` and `BenchmarkResult$score()` to allow to work with failing learners more diff --git a/R/BenchmarkResult.R b/R/BenchmarkResult.R index 270af360f..fb316aae1 100644 --- a/R/BenchmarkResult.R +++ b/R/BenchmarkResult.R @@ -4,8 +4,8 @@ #' #' @description #' This is the result container object returned by [benchmark()]. -#' A [BenchmarkResult] consists of the data row-binded data of multiple -#' [ResampleResult]s, which can easily be re-constructed. +#' A [BenchmarkResult] consists of the data of multiple +#' [ResampleResult]s. #' #' [BenchmarkResult]s can be visualized via \CRANpkg{mlr3viz}'s `autoplot()` function. #' diff --git a/R/LearnerClassifFeatureless.R b/R/LearnerClassifFeatureless.R index 193867eef..32d192ff7 100644 --- a/R/LearnerClassifFeatureless.R +++ b/R/LearnerClassifFeatureless.R @@ -4,7 +4,7 @@ #' @include LearnerClassif.R #' #' @description -#' A simple [LearnerClassif] which only analyses the labels during train, ignoring all features. +#' A simple [LearnerClassif] which only analyzes the labels during train, ignoring all features. #' Hyperparameter `method` determines the mode of operation during prediction: #' \describe{ #' \item{mode:}{Predicts the most frequent label. If there are two or more labels tied, randomly selects one per prediction.} diff --git a/R/LearnerRegrFeatureless.R b/R/LearnerRegrFeatureless.R index c29651b62..ef0896551 100644 --- a/R/LearnerRegrFeatureless.R +++ b/R/LearnerRegrFeatureless.R @@ -4,7 +4,7 @@ #' @include LearnerRegr.R #' #' @description -#' A simple [LearnerRegr] which only analyses the response during train, ignoring all features. +#' A simple [LearnerRegr] which only analyzes the response during train, ignoring all features. #' If hyperparameter `robust` is `FALSE` (default), constantly predicts `mean(y)` as response #' and `sd(y)` as standard error. #' If `robust` is `TRUE`, [median()] and [mad()] are used instead of [mean()] and [sd()], diff --git a/R/worker.R b/R/worker.R index 3fbee342f..7e1b4f7be 100644 --- a/R/worker.R +++ b/R/worker.R @@ -61,13 +61,15 @@ learner_train = function(learner, task, row_ids = NULL, mode = "train") { train_time = learner$state$train_time + result$elapsed } + proto = task$data(rows = integer()) learner$state = insert_named(learner$state, list( model = result$result, log = log, train_time = result$elapsed, param_vals = learner$param_set$values, task_hash = task$hash, - data_prototype = task$data(rows = integer()), + data_prototype = proto, + task_prototype = proto, # deprecated, remove for mlr3learners > 0.5.1 mlr3_version = packageVersion("mlr3") )) diff --git a/man/BenchmarkResult.Rd b/man/BenchmarkResult.Rd index 6a019781d..507900b65 100644 --- a/man/BenchmarkResult.Rd +++ b/man/BenchmarkResult.Rd @@ -5,8 +5,8 @@ \title{Container for Benchmarking Results} \description{ This is the result container object returned by \code{\link[=benchmark]{benchmark()}}. -A \link{BenchmarkResult} consists of the data row-binded data of multiple -\link{ResampleResult}s, which can easily be re-constructed. +A \link{BenchmarkResult} consists of the data of multiple +\link{ResampleResult}s. \link{BenchmarkResult}s can be visualized via \CRANpkg{mlr3viz}'s \code{autoplot()} function. diff --git a/man/mlr_learners_classif.featureless.Rd b/man/mlr_learners_classif.featureless.Rd index a7db90b6b..56655796d 100644 --- a/man/mlr_learners_classif.featureless.Rd +++ b/man/mlr_learners_classif.featureless.Rd @@ -5,7 +5,7 @@ \alias{LearnerClassifFeatureless} \title{Featureless Classification Learner} \description{ -A simple \link{LearnerClassif} which only analyses the labels during train, ignoring all features. +A simple \link{LearnerClassif} which only analyzes the labels during train, ignoring all features. Hyperparameter \code{method} determines the mode of operation during prediction: \describe{ \item{mode:}{Predicts the most frequent label. If there are two or more labels tied, randomly selects one per prediction.} diff --git a/man/mlr_learners_regr.featureless.Rd b/man/mlr_learners_regr.featureless.Rd index 06af61124..d9cf03fe6 100644 --- a/man/mlr_learners_regr.featureless.Rd +++ b/man/mlr_learners_regr.featureless.Rd @@ -5,7 +5,7 @@ \alias{LearnerRegrFeatureless} \title{Featureless Regression Learner} \description{ -A simple \link{LearnerRegr} which only analyses the response during train, ignoring all features. +A simple \link{LearnerRegr} which only analyzes the response during train, ignoring all features. If hyperparameter \code{robust} is \code{FALSE} (default), constantly predicts \code{mean(y)} as response and \code{sd(y)} as standard error. If \code{robust} is \code{TRUE}, \code{\link[=median]{median()}} and \code{\link[=mad]{mad()}} are used instead of \code{\link[=mean]{mean()}} and \code{\link[=sd]{sd()}}, diff --git a/man/mlr_measures_classif.acc.Rd b/man/mlr_measures_classif.acc.Rd index 8b65f4470..431c8d125 100644 --- a/man/mlr_measures_classif.acc.Rd +++ b/man/mlr_measures_classif.acc.Rd @@ -4,7 +4,12 @@ \alias{mlr_measures_classif.acc} \title{Classification Accuracy} \description{ -Classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in multiclass classification tasks. +} +\details{ +The Classification Accuracy is defined as \deqn{ \frac{1}{n} \sum_{i=1}^n w_i \left( t_i = r_i \right). }{ weighted.mean(t == r, w). diff --git a/man/mlr_measures_classif.auc.Rd b/man/mlr_measures_classif.auc.Rd index 2db5d4b4b..ae37f3765 100644 --- a/man/mlr_measures_classif.auc.Rd +++ b/man/mlr_measures_classif.auc.Rd @@ -4,11 +4,15 @@ \alias{mlr_measures_classif.auc} \title{Area Under the ROC Curve} \description{ +Measure to compare true observed labels with predicted +probabilities +in binary classification tasks. +} +\details{ Computes the area under the Receiver Operator Characteristic (ROC) curve. The AUC can be interpreted as the probability that a randomly chosen positive observation has a higher predicted probability than a randomly chosen negative observation. -} -\details{ + This measure is undefined if the true values are either all positive or all negative. } diff --git a/man/mlr_measures_classif.bacc.Rd b/man/mlr_measures_classif.bacc.Rd index 4a665ed5a..8c6a9067e 100644 --- a/man/mlr_measures_classif.bacc.Rd +++ b/man/mlr_measures_classif.bacc.Rd @@ -4,7 +4,12 @@ \alias{mlr_measures_classif.bacc} \title{Balanced Accuracy} \description{ -Computes the weighted balanced accuracy, suitable for imbalanced data sets. +Measure to compare true observed labels with predicted +labels +in multiclass classification tasks. +} +\details{ +The Balanced Accuracy computes the weighted balanced accuracy, suitable for imbalanced data sets. It is defined analogously to the definition in \href{https://scikit-learn.org/}{sklearn}. First, the sample weights \eqn{w} are normalized per class: diff --git a/man/mlr_measures_classif.bbrier.Rd b/man/mlr_measures_classif.bbrier.Rd index 65d697aba..9c2a2ca17 100644 --- a/man/mlr_measures_classif.bbrier.Rd +++ b/man/mlr_measures_classif.bbrier.Rd @@ -4,7 +4,12 @@ \alias{mlr_measures_classif.bbrier} \title{Binary Brier Score} \description{ -Brier score for binary classification problems defined as \deqn{ +Measure to compare true observed labels with predicted +probabilities +in binary classification tasks. +} +\details{ +The Binary Brier Score is defined as \deqn{ \frac{1}{n} \sum_{i=1}^n w_i (I_i - p_i)^2. }{ weighted.mean(((t == positive) - p)^2, w). diff --git a/man/mlr_measures_classif.ce.Rd b/man/mlr_measures_classif.ce.Rd index ef462d266..1c5f51bb1 100644 --- a/man/mlr_measures_classif.ce.Rd +++ b/man/mlr_measures_classif.ce.Rd @@ -4,7 +4,12 @@ \alias{mlr_measures_classif.ce} \title{Classification Error} \description{ -Classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in multiclass classification tasks. +} +\details{ +The Classification Error is defined as \deqn{ \frac{1}{n} \sum_{i=1}^n w_i \left( t_i \neq r_i \right). }{ weighted.mean(t != r, w). diff --git a/man/mlr_measures_classif.dor.Rd b/man/mlr_measures_classif.dor.Rd index 343fa6e19..145c2947f 100644 --- a/man/mlr_measures_classif.dor.Rd +++ b/man/mlr_measures_classif.dor.Rd @@ -4,13 +4,17 @@ \alias{mlr_measures_classif.dor} \title{Diagnostic Odds Ratio} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The Diagnostic Odds Ratio is defined as \deqn{ \frac{\mathrm{TP}/\mathrm{FP}}{\mathrm{FN}/\mathrm{TN}}. }{ (TP/FP) / (FN/TN). } -} -\details{ + This measure is undefined if FP = 0 or FN = 0. } \note{ diff --git a/man/mlr_measures_classif.fbeta.Rd b/man/mlr_measures_classif.fbeta.Rd index 5c4cf5889..830d3ec2a 100644 --- a/man/mlr_measures_classif.fbeta.Rd +++ b/man/mlr_measures_classif.fbeta.Rd @@ -4,8 +4,12 @@ \alias{mlr_measures_classif.fbeta} \title{F-beta Score} \description{ -Binary classification measure defined with \eqn{P} as \code{\link[mlr3measures:ppv]{precision()}} and \eqn{R} as -\code{\link[mlr3measures:tpr]{recall()}} as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +With \eqn{P} as \code{\link[mlr3measures:ppv]{precision()}} and \eqn{R} as \code{\link[mlr3measures:tpr]{recall()}}, the F-beta Score is defined as \deqn{ (1 + \beta^2) \frac{P \cdot R}{(\beta^2 P) + R}. }{ (1 + beta^2) * (P*R) / ((beta^2 * P) + R). @@ -13,13 +17,8 @@ Binary classification measure defined with \eqn{P} as \code{\link[mlr3measures:p It measures the effectiveness of retrieval with respect to a user who attaches \eqn{\beta}{beta} times as much importance to recall as precision. For \eqn{\beta = 1}{beta = 1}, this measure is called "F1" score. -} -\details{ -This measure is undefined if -\itemize{ -\item TP = 0 -\item \link[mlr3measures]{precision} or \link[mlr3measures]{recall} is undefined, i.e. TP + FP = 0 or TP + FN = 0. -} + +This measure is undefined if \link[mlr3measures]{precision} or \link[mlr3measures]{recall} is undefined, i.e. TP + FP = 0 or TP + FN = 0. } \note{ The score function calls \code{\link[mlr3measures:fbeta]{mlr3measures::fbeta()}} from package \CRANpkg{mlr3measures}. diff --git a/man/mlr_measures_classif.fdr.Rd b/man/mlr_measures_classif.fdr.Rd index 3f7c74571..f1d5bacf5 100644 --- a/man/mlr_measures_classif.fdr.Rd +++ b/man/mlr_measures_classif.fdr.Rd @@ -4,13 +4,17 @@ \alias{mlr_measures_classif.fdr} \title{False Discovery Rate} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The False Discovery Rate is defined as \deqn{ \frac{\mathrm{FP}}{\mathrm{TP} + \mathrm{FP}}. }{ FP / (TP + FP). } -} -\details{ + This measure is undefined if TP + FP = 0. } \note{ diff --git a/man/mlr_measures_classif.fn.Rd b/man/mlr_measures_classif.fn.Rd index a2d15ad9b..f9e4a7b57 100644 --- a/man/mlr_measures_classif.fn.Rd +++ b/man/mlr_measures_classif.fn.Rd @@ -4,7 +4,12 @@ \alias{mlr_measures_classif.fn} \title{False Negatives} \description{ -Classification measure counting the false negatives (type 2 error), i.e. the number of +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +This measure counts the false negatives (type 2 error), i.e. the number of predictions indicating a negative class label while in fact it is positive. This is sometimes also called a "false alarm". } diff --git a/man/mlr_measures_classif.fnr.Rd b/man/mlr_measures_classif.fnr.Rd index 2b8b4ac53..9eb3ea79b 100644 --- a/man/mlr_measures_classif.fnr.Rd +++ b/man/mlr_measures_classif.fnr.Rd @@ -4,14 +4,18 @@ \alias{mlr_measures_classif.fnr} \title{False Negative Rate} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The False Negative Rate is defined as \deqn{ \frac{\mathrm{FN}}{\mathrm{TP} + \mathrm{FN}}. }{ FN / (TP + FN). } Also know as "miss rate". -} -\details{ + This measure is undefined if TP + FN = 0. } \note{ diff --git a/man/mlr_measures_classif.fomr.Rd b/man/mlr_measures_classif.fomr.Rd index d9aa3ca9b..299517148 100644 --- a/man/mlr_measures_classif.fomr.Rd +++ b/man/mlr_measures_classif.fomr.Rd @@ -4,13 +4,17 @@ \alias{mlr_measures_classif.fomr} \title{False Omission Rate} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The False Omission Rate is defined as \deqn{ \frac{\mathrm{FN}}{\mathrm{FN} + \mathrm{TN}}. }{ FN / (FN + TN). } -} -\details{ + This measure is undefined if FN + TN = 0. } \note{ diff --git a/man/mlr_measures_classif.fp.Rd b/man/mlr_measures_classif.fp.Rd index 82e881d89..3ad8a6858 100644 --- a/man/mlr_measures_classif.fp.Rd +++ b/man/mlr_measures_classif.fp.Rd @@ -4,7 +4,12 @@ \alias{mlr_measures_classif.fp} \title{False Positives} \description{ -Classification measure counting the false positives (type 1 error), i.e. the number of +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +This measure counts the false positives (type 1 error), i.e. the number of predictions indicating a positive class label while in fact it is negative. } \note{ diff --git a/man/mlr_measures_classif.fpr.Rd b/man/mlr_measures_classif.fpr.Rd index 77226620d..24b08fccd 100644 --- a/man/mlr_measures_classif.fpr.Rd +++ b/man/mlr_measures_classif.fpr.Rd @@ -4,14 +4,18 @@ \alias{mlr_measures_classif.fpr} \title{False Positive Rate} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The False Positive Rate is defined as \deqn{ \frac{\mathrm{FP}}{\mathrm{FP} + \mathrm{TN}}. }{ FP / (FP + TN). } Also know as fall out or probability of false alarm. -} -\details{ + This measure is undefined if FP + TN = 0. } \note{ diff --git a/man/mlr_measures_classif.logloss.Rd b/man/mlr_measures_classif.logloss.Rd index 271f49082..35f00a0f4 100644 --- a/man/mlr_measures_classif.logloss.Rd +++ b/man/mlr_measures_classif.logloss.Rd @@ -4,7 +4,12 @@ \alias{mlr_measures_classif.logloss} \title{Log Loss} \description{ -Classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +probabilities +in multiclass classification tasks. +} +\details{ +The Log Loss is defined as \deqn{ -\frac{1}{n} \sum_{i=1}^n w_i \log \left( p_i \right ) }{ -weighted.mean(log(p), w) diff --git a/man/mlr_measures_classif.mbrier.Rd b/man/mlr_measures_classif.mbrier.Rd index 258279522..2c6d181ab 100644 --- a/man/mlr_measures_classif.mbrier.Rd +++ b/man/mlr_measures_classif.mbrier.Rd @@ -4,6 +4,11 @@ \alias{mlr_measures_classif.mbrier} \title{Multiclass Brier Score} \description{ +Measure to compare true observed labels with predicted +probabilities +in multiclass classification tasks. +} +\details{ Brier score for multi-class classification problems with \eqn{r} labels defined as \deqn{ \frac{1}{n} \sum_{i=1}^n \sum_{j=1}^r (I_{ij} - p_{ij})^2. }{ diff --git a/man/mlr_measures_classif.mcc.Rd b/man/mlr_measures_classif.mcc.Rd index eb59ada44..48221fe5a 100644 --- a/man/mlr_measures_classif.mcc.Rd +++ b/man/mlr_measures_classif.mcc.Rd @@ -4,13 +4,17 @@ \alias{mlr_measures_classif.mcc} \title{Matthews Correlation Coefficient} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The Matthews Correlation Coefficient is defined as \deqn{ \frac{\mathrm{TP} \cdot \mathrm{TN} - \mathrm{FP} \cdot \mathrm{FN}}{\sqrt{(\mathrm{TP} + \mathrm{FP}) (\mathrm{TP} + \mathrm{FN}) (\mathrm{TN} + \mathrm{FP}) (\mathrm{TN} + \mathrm{FN})}}. }{ (TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN)). } -} -\details{ + This above formula is undefined if any of the four sums in the denominator is 0. The denominator is then set to 1. } diff --git a/man/mlr_measures_classif.npv.Rd b/man/mlr_measures_classif.npv.Rd index 999190417..cb7983ec8 100644 --- a/man/mlr_measures_classif.npv.Rd +++ b/man/mlr_measures_classif.npv.Rd @@ -4,13 +4,17 @@ \alias{mlr_measures_classif.npv} \title{Negative Predictive Value} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The Negative Predictive Value is defined as \deqn{ \frac{\mathrm{TN}}{\mathrm{FN} + \mathrm{TN}}. }{ TN / (FN + TN). } -} -\details{ + This measure is undefined if FN + TN = 0. } \note{ diff --git a/man/mlr_measures_classif.ppv.Rd b/man/mlr_measures_classif.ppv.Rd index 9a82baa50..c908d5b1d 100644 --- a/man/mlr_measures_classif.ppv.Rd +++ b/man/mlr_measures_classif.ppv.Rd @@ -4,14 +4,18 @@ \alias{mlr_measures_classif.ppv} \title{Positive Predictive Value} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The Positive Predictive Value is defined as \deqn{ \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FP}}. }{ TP / (TP + FP). } Also know as "precision". -} -\details{ + This measure is undefined if TP + FP = 0. } \note{ diff --git a/man/mlr_measures_classif.prauc.Rd b/man/mlr_measures_classif.prauc.Rd index 3ec8a32c5..291f25ce7 100644 --- a/man/mlr_measures_classif.prauc.Rd +++ b/man/mlr_measures_classif.prauc.Rd @@ -4,12 +4,16 @@ \alias{mlr_measures_classif.prauc} \title{Area Under the Precision-Recall Curve} \description{ +Measure to compare true observed labels with predicted +probabilities +in binary classification tasks. +} +\details{ Computes the area under the Precision-Recall curve (PRC). The PRC can be interpreted as the relationship between precision and recall (sensitivity), and is considered to be a more appropriate measure for unbalanced datasets than the ROC curve. The PRC is computed by integration of the piecewise function. -} -\details{ + This measure is undefined if the true values are either all positive or all negative. } diff --git a/man/mlr_measures_classif.precision.Rd b/man/mlr_measures_classif.precision.Rd index ec4557860..317ac951f 100644 --- a/man/mlr_measures_classif.precision.Rd +++ b/man/mlr_measures_classif.precision.Rd @@ -4,14 +4,18 @@ \alias{mlr_measures_classif.precision} \title{Positive Predictive Value} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The Positive Predictive Value is defined as \deqn{ \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FP}}. }{ TP / (TP + FP). } Also know as "precision". -} -\details{ + This measure is undefined if TP + FP = 0. } \note{ diff --git a/man/mlr_measures_classif.recall.Rd b/man/mlr_measures_classif.recall.Rd index f9f198598..c2f179dce 100644 --- a/man/mlr_measures_classif.recall.Rd +++ b/man/mlr_measures_classif.recall.Rd @@ -4,14 +4,18 @@ \alias{mlr_measures_classif.recall} \title{True Positive Rate} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The True Positive Rate is defined as \deqn{ \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}. }{ TP / (TP + FN). } Also know as "recall" or "sensitivity". -} -\details{ + This measure is undefined if TP + FN = 0. } \note{ diff --git a/man/mlr_measures_classif.sensitivity.Rd b/man/mlr_measures_classif.sensitivity.Rd index bcd970acd..6ac361452 100644 --- a/man/mlr_measures_classif.sensitivity.Rd +++ b/man/mlr_measures_classif.sensitivity.Rd @@ -4,14 +4,18 @@ \alias{mlr_measures_classif.sensitivity} \title{True Positive Rate} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The True Positive Rate is defined as \deqn{ \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}. }{ TP / (TP + FN). } Also know as "recall" or "sensitivity". -} -\details{ + This measure is undefined if TP + FN = 0. } \note{ diff --git a/man/mlr_measures_classif.specificity.Rd b/man/mlr_measures_classif.specificity.Rd index 652367bc9..026c1c612 100644 --- a/man/mlr_measures_classif.specificity.Rd +++ b/man/mlr_measures_classif.specificity.Rd @@ -4,14 +4,18 @@ \alias{mlr_measures_classif.specificity} \title{True Negative Rate} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The True Negative Rate is defined as \deqn{ \frac{\mathrm{TN}}{\mathrm{FP} + \mathrm{TN}}. }{ TN / (FP + TN). } Also know as "specificity". -} -\details{ + This measure is undefined if FP + TN = 0. } \note{ diff --git a/man/mlr_measures_classif.tn.Rd b/man/mlr_measures_classif.tn.Rd index 4d60ab70d..aed9c9d3e 100644 --- a/man/mlr_measures_classif.tn.Rd +++ b/man/mlr_measures_classif.tn.Rd @@ -4,7 +4,12 @@ \alias{mlr_measures_classif.tn} \title{True Negatives} \description{ -Classification measure counting the true negatives, i.e. the number of +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +This measure counts the true negatives, i.e. the number of predictions correctly indicating a negative class label. } \note{ diff --git a/man/mlr_measures_classif.tnr.Rd b/man/mlr_measures_classif.tnr.Rd index e6ca624c1..855a552f3 100644 --- a/man/mlr_measures_classif.tnr.Rd +++ b/man/mlr_measures_classif.tnr.Rd @@ -4,14 +4,18 @@ \alias{mlr_measures_classif.tnr} \title{True Negative Rate} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The True Negative Rate is defined as \deqn{ \frac{\mathrm{TN}}{\mathrm{FP} + \mathrm{TN}}. }{ TN / (FP + TN). } Also know as "specificity". -} -\details{ + This measure is undefined if FP + TN = 0. } \note{ diff --git a/man/mlr_measures_classif.tp.Rd b/man/mlr_measures_classif.tp.Rd index a797a2b83..4004ec2ce 100644 --- a/man/mlr_measures_classif.tp.Rd +++ b/man/mlr_measures_classif.tp.Rd @@ -4,7 +4,12 @@ \alias{mlr_measures_classif.tp} \title{True Positives} \description{ -Binary classification measure counting the true positives, i.e. the number of +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +This measure counts the true positives, i.e. the number of predictions correctly indicating a positive class label. } \note{ diff --git a/man/mlr_measures_classif.tpr.Rd b/man/mlr_measures_classif.tpr.Rd index c50ad867b..e80c0cb4d 100644 --- a/man/mlr_measures_classif.tpr.Rd +++ b/man/mlr_measures_classif.tpr.Rd @@ -4,14 +4,18 @@ \alias{mlr_measures_classif.tpr} \title{True Positive Rate} \description{ -Binary classification measure defined as \deqn{ +Measure to compare true observed labels with predicted +labels +in binary classification tasks. +} +\details{ +The True Positive Rate is defined as \deqn{ \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}. }{ TP / (TP + FN). } Also know as "recall" or "sensitivity". -} -\details{ + This measure is undefined if TP + FN = 0. } \note{ diff --git a/man/mlr_measures_regr.bias.Rd b/man/mlr_measures_regr.bias.Rd index f05614bdd..6b85625b9 100644 --- a/man/mlr_measures_regr.bias.Rd +++ b/man/mlr_measures_regr.bias.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.bias} \title{Bias} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Bias is defined as \deqn{ \frac{1}{n} \sum_{i=1}^n w_i \left( t_i - r_i \right). }{ weighted.mean(t - r, w). diff --git a/man/mlr_measures_regr.ktau.Rd b/man/mlr_measures_regr.ktau.Rd index e634b918f..e3f0910e4 100644 --- a/man/mlr_measures_regr.ktau.Rd +++ b/man/mlr_measures_regr.ktau.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.ktau} \title{Kendall's tau} \description{ -Regression measure defined as Kendall's rank correlation coefficient between truth and response. +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +Kendall's tau is defined as Kendall's rank correlation coefficient between truth and response. Calls \code{\link[stats:cor]{stats::cor()}} with \code{method} set to \code{"kendall"}. } \note{ diff --git a/man/mlr_measures_regr.mae.Rd b/man/mlr_measures_regr.mae.Rd index cc1c84163..67106f3e1 100644 --- a/man/mlr_measures_regr.mae.Rd +++ b/man/mlr_measures_regr.mae.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.mae} \title{Mean Absolute Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Mean Absolute Error is defined as \deqn{ \frac{1}{n} \sum_{i=1}^n w_i \left| t_i - r_i \right|. }{ weighted.mean(abs(t - r), w). diff --git a/man/mlr_measures_regr.mape.Rd b/man/mlr_measures_regr.mape.Rd index c5c05c906..278e73614 100644 --- a/man/mlr_measures_regr.mape.Rd +++ b/man/mlr_measures_regr.mape.Rd @@ -4,13 +4,15 @@ \alias{mlr_measures_regr.mape} \title{Mean Absolute Percent Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Mean Absolute Percent Error is defined as \deqn{ \frac{1}{n} \sum_{i=1}^n w_i \left| \frac{ t_i - r_i}{t_i} \right|. }{ weighted.mean(abs((t - r) / t), w). } -} -\details{ + This measure is undefined if any element of \eqn{t} is \eqn{0}. } \note{ diff --git a/man/mlr_measures_regr.maxae.Rd b/man/mlr_measures_regr.maxae.Rd index a1c753647..cbb56d916 100644 --- a/man/mlr_measures_regr.maxae.Rd +++ b/man/mlr_measures_regr.maxae.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.maxae} \title{Max Absolute Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Max Absolute Error is defined as \deqn{ \max \left( \left| t_i - r_i \right| \right). }{ max(abs(t - r)). diff --git a/man/mlr_measures_regr.medae.Rd b/man/mlr_measures_regr.medae.Rd index d247220a4..1a059bf6d 100644 --- a/man/mlr_measures_regr.medae.Rd +++ b/man/mlr_measures_regr.medae.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.medae} \title{Median Absolute Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Median Absolute Error is defined as \deqn{ \mathop{\mathrm{median}}_i \left| t_i - r_i \right|. }{ median(abs(t - r)). diff --git a/man/mlr_measures_regr.medse.Rd b/man/mlr_measures_regr.medse.Rd index e46c9f507..fd17a662c 100644 --- a/man/mlr_measures_regr.medse.Rd +++ b/man/mlr_measures_regr.medse.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.medse} \title{Median Squared Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Median Squared Error is defined as \deqn{ \mathop{\mathrm{median}}_i \left[ \left( t_i - r_i \right)^2 \right]. }{ median((t - r)^2). diff --git a/man/mlr_measures_regr.mse.Rd b/man/mlr_measures_regr.mse.Rd index 4b37880d2..48eeaba35 100644 --- a/man/mlr_measures_regr.mse.Rd +++ b/man/mlr_measures_regr.mse.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.mse} \title{Mean Squared Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Mean Squared Error is defined as \deqn{ \frac{1}{n} w_i \sum_{i=1}^n \left( t_i - r_i \right)^2. }{ weighted.mean((t - r)^2, w). diff --git a/man/mlr_measures_regr.msle.Rd b/man/mlr_measures_regr.msle.Rd index 17bc4680b..86b5bc6dc 100644 --- a/man/mlr_measures_regr.msle.Rd +++ b/man/mlr_measures_regr.msle.Rd @@ -4,13 +4,15 @@ \alias{mlr_measures_regr.msle} \title{Mean Squared Log Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Mean Squared Log Error is defined as \deqn{ \frac{1}{n} \sum_{i=1}^n w_i \left( \ln (1 + t_i) - \ln (1 + r_i) \right)^2. }{ weighted.mean((log(1 + t) - log(1 + r))^2, weights). } -} -\details{ + This measure is undefined if any element of \eqn{t} or \eqn{r} is less than or equal to \eqn{-1}. } \note{ diff --git a/man/mlr_measures_regr.pbias.Rd b/man/mlr_measures_regr.pbias.Rd index 72cbac13c..b6468c7ba 100644 --- a/man/mlr_measures_regr.pbias.Rd +++ b/man/mlr_measures_regr.pbias.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.pbias} \title{Percent Bias} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Percent Bias is defined as \deqn{ \frac{1}{n} \sum_{i=1}^n w_i \frac{\left( t_i - r_i \right)}{\left| t_i \right|}. }{ weighted.mean((t - r) / abs(t), w). diff --git a/man/mlr_measures_regr.rae.Rd b/man/mlr_measures_regr.rae.Rd index 551600d09..70f25ef56 100644 --- a/man/mlr_measures_regr.rae.Rd +++ b/man/mlr_measures_regr.rae.Rd @@ -4,14 +4,16 @@ \alias{mlr_measures_regr.rae} \title{Relative Absolute Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Relative Absolute Error is defined as \deqn{ \frac{\sum_{i=1}^n \left| t_i - r_i \right|}{\sum_{i=1}^n \left| t_i - \bar{t} \right|}. }{ sum((t - r)^2) / sum((t - mean(t))^2). } Can be interpreted as absolute error of the predictions relative to a naive model predicting the mean. -} -\details{ + This measure is undefined for constant \eqn{t}. } \note{ diff --git a/man/mlr_measures_regr.rmse.Rd b/man/mlr_measures_regr.rmse.Rd index ccdbe00a0..4d90a1f8b 100644 --- a/man/mlr_measures_regr.rmse.Rd +++ b/man/mlr_measures_regr.rmse.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.rmse} \title{Root Mean Squared Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Root Mean Squared Error is defined as \deqn{ \sqrt{\frac{1}{n} \sum_{i=1}^n w_i \left( t_i - r_i \right)^2}. }{ sqrt(weighted.mean((t - r)^2, w)). diff --git a/man/mlr_measures_regr.rmsle.Rd b/man/mlr_measures_regr.rmsle.Rd index 2d904bd34..3ff110578 100644 --- a/man/mlr_measures_regr.rmsle.Rd +++ b/man/mlr_measures_regr.rmsle.Rd @@ -4,13 +4,15 @@ \alias{mlr_measures_regr.rmsle} \title{Root Mean Squared Log Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Root Mean Squared Log Error is defined as \deqn{ \sqrt{\frac{1}{n} \sum_{i=1}^n w_i \left( \ln (1 + t_i) - \ln (1 + r_i) \right)^2}. }{ sqrt(weighted.mean((log(1 + t) - log(1 + r))^2, w)). } -} -\details{ + This measure is undefined if any element of \eqn{t} or \eqn{r} is less than or equal to \eqn{-1}. } \note{ diff --git a/man/mlr_measures_regr.rrse.Rd b/man/mlr_measures_regr.rrse.Rd index 16bcd6240..c96fce53d 100644 --- a/man/mlr_measures_regr.rrse.Rd +++ b/man/mlr_measures_regr.rrse.Rd @@ -4,14 +4,16 @@ \alias{mlr_measures_regr.rrse} \title{Root Relative Squared Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Root Relative Squared Error is defined as \deqn{ \sqrt{\frac{\sum_{i=1}^n \left( t_i - r_i \right)^2}{\sum_{i=1}^n \left( t_i - \bar{t} \right)^2}}. }{ sqrt(sum((t - r)^2) / sum((t - mean(t))^2)). } Can be interpreted as root of the squared error of the predictions relative to a naive model predicting the mean. -} -\details{ + This measure is undefined for constant \eqn{t}. } \note{ diff --git a/man/mlr_measures_regr.rse.Rd b/man/mlr_measures_regr.rse.Rd index 891e0d6f5..62e93253c 100644 --- a/man/mlr_measures_regr.rse.Rd +++ b/man/mlr_measures_regr.rse.Rd @@ -4,14 +4,16 @@ \alias{mlr_measures_regr.rse} \title{Relative Squared Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Relative Squared Error is defined as \deqn{ \frac{\sum_{i=1}^n \left( t_i - r_i \right)^2}{\sum_{i=1}^n \left( t_i - \bar{t} \right)^2}. }{ sum((t - r)^2) / sum((t - mean(t))^2). } Can be interpreted as squared error of the predictions relative to a naive model predicting the mean. -} -\details{ + This measure is undefined for constant \eqn{t}. } \note{ diff --git a/man/mlr_measures_regr.rsq.Rd b/man/mlr_measures_regr.rsq.Rd index fcc61142d..717fe6f76 100644 --- a/man/mlr_measures_regr.rsq.Rd +++ b/man/mlr_measures_regr.rsq.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.rsq} \title{R Squared} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +R Squared is defined as \deqn{ 1 - \frac{\sum_{i=1}^n \left( t_i - r_i \right)^2}{\sum_{i=1}^n \left( t_i - \bar{t} \right)^2}. }{ 1 - sum((t - r)^2) / sum((t - mean(t))^2). @@ -12,8 +15,7 @@ Regression measure defined as \deqn{ Also known as coefficient of determination or explained variation. Subtracts the \code{\link[mlr3measures:rse]{rse()}} from 1, hence it compares the squared error of the predictions relative to a naive model predicting the mean. -} -\details{ + This measure is undefined for constant \eqn{t}. } \note{ diff --git a/man/mlr_measures_regr.sae.Rd b/man/mlr_measures_regr.sae.Rd index e556a9ccc..df9497751 100644 --- a/man/mlr_measures_regr.sae.Rd +++ b/man/mlr_measures_regr.sae.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.sae} \title{Sum of Absolute Errors} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Sum of Absolute Errors is defined as \deqn{ \sum_{i=1}^n \left| t_i - r_i \right|. }{ sum(abs((t - r))). diff --git a/man/mlr_measures_regr.smape.Rd b/man/mlr_measures_regr.smape.Rd index e1c8a02c1..6e72bd5ba 100644 --- a/man/mlr_measures_regr.smape.Rd +++ b/man/mlr_measures_regr.smape.Rd @@ -4,13 +4,15 @@ \alias{mlr_measures_regr.smape} \title{Symmetric Mean Absolute Percent Error} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Symmetric Mean Absolute Percent Error is defined as \deqn{ \frac{2}{n} \sum_{i=1}^n \frac{\left| t_i - r_i \right|}{\left| t_i \right| + \left| r_i \right|}. }{ 2 * mean(abs(t - r) / (abs(t) + abs(r))). } -} -\details{ + This measure is undefined if if any \eqn{|t| + |r|} is \eqn{0}. } \note{ diff --git a/man/mlr_measures_regr.srho.Rd b/man/mlr_measures_regr.srho.Rd index 0f4fde149..e03a2c608 100644 --- a/man/mlr_measures_regr.srho.Rd +++ b/man/mlr_measures_regr.srho.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.srho} \title{Spearman's rho} \description{ -Regression measures defined as Spearman's rank correlation coefficient between truth and response. +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +Spearman's rho is defined as Spearman's rank correlation coefficient between truth and response. Calls \code{\link[stats:cor]{stats::cor()}} with \code{method} set to \code{"spearman"}. } \note{ diff --git a/man/mlr_measures_regr.sse.Rd b/man/mlr_measures_regr.sse.Rd index f2dd90551..9d59a172b 100644 --- a/man/mlr_measures_regr.sse.Rd +++ b/man/mlr_measures_regr.sse.Rd @@ -4,7 +4,10 @@ \alias{mlr_measures_regr.sse} \title{Sum of Squared Errors} \description{ -Regression measure defined as \deqn{ +Measure to compare true observed response with predicted response in regression tasks. +} +\details{ +The Sum of Squared Errors is defined as \deqn{ \sum_{i=1}^n \left( t_i - r_i \right)^2. }{ sum((t - r)^2). diff --git a/man/mlr_measures_sim.jaccard.Rd b/man/mlr_measures_sim.jaccard.Rd index 61e7fd536..3d7e3a54e 100644 --- a/man/mlr_measures_sim.jaccard.Rd +++ b/man/mlr_measures_sim.jaccard.Rd @@ -5,7 +5,9 @@ \title{Jaccard Similarity Index} \description{ Measure to compare two or more sets w.r.t. their similarity. -For two sets \eqn{A} and \eqn{B}, it is defined as +} +\details{ +For two sets \eqn{A} and \eqn{B}, the Jaccard Index is defined as \deqn{ J(A, B) = \frac{|A \cap B|}{|A \cup B|}. }{ @@ -13,8 +15,7 @@ For two sets \eqn{A} and \eqn{B}, it is defined as } If more than two sets are provided, the mean of all pairwise scores is calculated. -} -\details{ + This measure is undefined if two or more sets are empty. } \note{ diff --git a/man/mlr_measures_sim.phi.Rd b/man/mlr_measures_sim.phi.Rd index 4760779ed..cb624af64 100644 --- a/man/mlr_measures_sim.phi.Rd +++ b/man/mlr_measures_sim.phi.Rd @@ -5,7 +5,9 @@ \title{Phi Coefficient Similarity} \description{ Measure to compare two or more sets w.r.t. their similarity. -It is defined as the Pearson correlation between the binary +} +\details{ +The Phi Coefficient is defined as the Pearson correlation between the binary representation of two sets \eqn{A} and \eqn{B}. The binary representation for \eqn{A} is a logical vector of length \eqn{p} with the i-th element being 1 if the corresponding @@ -13,8 +15,7 @@ element is in \eqn{A}, and 0 otherwise. If more than two sets are provided, the mean of all pairwise scores is calculated. -} -\details{ + This measure is undefined if one set contains none or all possible elements. } \note{