-
A new function
bound_prediction()
is available to constrain the values of a numeric prediction (#142). -
Fixed a bug where non-standard names of class probability estimates resulted in an error for some calibration models (#145).
-
Bug fix for
cal_plot_breaks()
with binary classification with custom probability column names (#144). -
Fixed an error in
int_conformal_cv()
when grouped resampling was used (#141). -
Fixed an issue where the
distance
metric appeared inconsistently when usingthreshold_perf()
with custom metric sets (@jrwinget, #149).
- Fixed a bug where the grouping for calibration methods was sensitive to the type of the grouping variables (#127).
- Quick release to remove the base R pipe (to maintain backwards compatibility).
-
The conformal functions
int_conformal_infer_*()
were renamed toint_conformal_*()
. -
predict.int_conformal_cv()
now returns a.pred
column that is the average prediction from the resampled models. The prediction intervals are centered on these. -
Split conformal inference is available using
int_conformal_split()
and conformal quantile regression can be used withint_conformal_quantile()
.
-
Copyright holder changed to Posit Software PBC.
-
A set of calibration tools were added:
- The need for calibration can be visualized using the collection of
cal_plot_*()
functions. - Calibration methods can be estimated with a family of
cal_estimate_*()
functions. - To validate the calibrations using resampling, see the
cal_validate_*()
functions. cal_apply()
can take a calibration model and apply it to a set of existing predictions.
- The need for calibration can be visualized using the collection of
-
Possible calibration tools:
- Binary classification methods: logistic regression, isotonic regression, and Beta calibration.
- Multiclass classification: multinomial, isotonic regression, and Beta calibration
- Regression: linear regression, isotonic regression
-
Based on the initial PR (#37) by Antonio R. Vargas,
threshold_perf()
now accepts a custom metric set (#25) -
Two functions were added to compute prediction intervals for regression models via conformal inference:
int_conformal_infer()
int_conformal_infer_cv()
-
Max Kuhn is now the maintainer (#49).
-
Re-licensed package from GPL-2 to MIT. All copyright holders are RStudio employees and give consent.
-
Fixed a bug with how
make_class_pred()
andmake_two_class_pred()
validate thelevels
argument (#42). -
threshold_perf()
now has an explicitevent_level
argument rather than respecting the now deprecatedyardstick.event_first
global option (#45). -
Bumped the minimum required R version to >=3.4.0 to align with the rest of the tidyverse.
-
Updated to testthat 3e (#44).
class_pred
objects are now comparable and will be ordered by their levels. Equivocal values are generally considered to be the smallest value when ordering.NA
values can be considered smaller ifvec_order(na_value = "smallest")
is used.
- Internal cleanup to be more compatible with vctrs 0.3.0.
-
Suggest the modeldata package, which is where the
lending_club
dataset has been moved after being removed from recipes. -
Use
testthat::verify_output()
on a test expecting a specific vctrs error to avoid failure on CRAN if that error changes in the future.
-
probably has been brought up to date with vctrs 0.2.0. This vctrs update had many function name changes, and required internal refactoring, but there should be minimal external changes.
-
The one user facing change comes with casting from one
class_pred
object to anotherclass_pred
, or to afactor
. Where previously a warning would be thrown ifx
had levels that did not exist into
, an error is now generated. This is consistent with the vctrs behavior when converting from one factor to another.x <- class_pred(factor("a")) to <- class_pred(factor("to")) vec_cast(x, to) #> Error: Lossy cast from <class_pred> to <class_pred>. #> Locations: 1
-
A failing test relying on the R 3.6 change to
sample()
has been corrected. -
An rlang warning in
threshold_perf()
has been fixed. -
A small R 3.1 issue with vctrs has been fixed.
- First release