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Merge pull request #47 from mlr-org/release
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release: 0.6.0
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be-marc authored Jul 22, 2024
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4 changes: 4 additions & 0 deletions .Rbuildignore
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^\.ccache$
^\.lintr$
^docs$
^scratch$
^revdep$
^CRAN-SUBMISSION$
^cran-comments\.md$
97 changes: 96 additions & 1 deletion .gitignore
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25 changes: 11 additions & 14 deletions DESCRIPTION
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Package: mlr3measures
Title: Performance Measures for 'mlr3'
Version: 0.5.0-9000
Authors@R:
c(person(given = "Michel",
family = "Lang",
role = c("cre", "aut"),
email = "michellang@gmail.com",
comment = c(ORCID = "0000-0001-9754-0393")),
person(given = "Martin",
family = "Binder",
role = "ctb",
email = "mlr.developer@mb706.com"))
Description: Implements multiple performance measures for
supervised learning. Includes over 40 measures for regression and
Version: 0.6.0
Authors@R:c(
person("Michel", "Lang", , "michellang@gmail.com", role = "aut",
comment = c(ORCID = "0000-0001-9754-0393")),
person("Martin", "Binder", , "mlr.developer@mb706.com", role = "ctb"),
person("Marc", "Becker", , "marcbecker@posteo.de", role = c("cre", "aut"),
comment = c(ORCID = "0000-0002-8115-0400"))
)
Description: Implements multiple performance measures for supervised
learning. Includes over 40 measures for regression and
classification. Additionally, meta information about the performance
measures can be queried, e.g. what the best and worst possible
performances scores are.
Expand All @@ -27,8 +24,8 @@ Imports:
PRROC
Suggests:
testthat (>= 3.0.0)
Encoding: UTF-8
Config/testthat/edition: 3
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
Collate:
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8 changes: 4 additions & 4 deletions NEWS.md
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# mlr3measures 0.6.0

* Added binary classification measures `gmean` and `gpr`.
* Added multiclass `mcc`.

# mlr3measures 0.5.0

* Added some observation-wise loss functions: `ae`, `ape`, `se`,
`sle`, and `zero_one`,
* Added some observation-wise loss functions: `ae`, `ape`, `se`,
`sle`, and `zero_one`,

# mlr3measures 0.4.1

* Calculation of `fbeta` is now more numerically more stable.
* Improved documentation.


# mlr3measures 0.4.0

* New measures to calculate set similarities: `jaccard` and `phi`.
* Many measures now support sample weights.
Supported measures have `$sample_weights` set to `TRUE` in the `measures`
environment.


# mlr3measures 0.3.1

* Fix edge case for `msle` and `rmsle`.
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6 changes: 3 additions & 3 deletions R/classif_mcc.R
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#' }
#' where \eqn{TP}, \eqn{FP}, \eqn{TN}, \eqn{TP} are the number of true positives, false positives, true negatives, and false negatives respectively.
#'
#' In the multi-class case, the Matthews Correlation Coefficient defined for a multi-class confusion matrix \enq{C} with \eqn{K} classes: \deqn{
#' In the multi-class case, the Matthews Correlation Coefficient defined for a multi-class confusion matrix \eqn{C} with \eqn{K} classes: \deqn{
#' \frac{c \cdot s - \sum_k^K p_k \cdot t_k}{\sqrt{(s^2 - \sum_k^K p_k^2) \cdot (s^2 - \sum_k^K t_k^2)}},
#' }{
#' (c * s - sum(pk * tk)) / sqrt((s^2 - sum(pk^2)) * (s^2 - sum(tk^2))),
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#' @details
#' The above formula is undefined if any of the four sums in the denominator is 0 in the binary case and more generally if either \eqn{s^2 - sum(pk^2)} or \eqn{s^2 - sum(tk^2)} is equal to 0.
#' The denominator is then set to 1.
#' When there are more than two classes, the MCC will no longer range between 1 and +1.
#' Instead, the minimum value will be between 1 and 0 depending on the true distribution. The maximum value is always +1.
#' When there are more than two classes, the MCC will no longer range between -1 and +1.
#' Instead, the minimum value will be between -1 and 0 depending on the true distribution. The maximum value is always +1.
#'
#' @references
#' \url{https://en.wikipedia.org/wiki/Phi_coefficient}
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