Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. 'probably' contains tools for conducting these operations.
probably contains tools to facilitate activities such as:
Conversion of probabilities to discrete class predictions.
Investigating and estimating optimal probability thresholds.
Inclusion of equivocal zones where the probabilities are too uncertain to report a prediction.
You can install probably from CRAN with:
You can install the development version of probably from GitHub with:
Good places to look for examples of using probably are the vignettes.
vignette("equivocal-zones", "probably") discusses the new
class_pred class that probably provides for working with equivocal
vignette("where-to-use", "probably") discusses how probably fits
in with the rest of the tidymodels ecosystem, and provides an
example of optimizing class probability thresholds.
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.