Tailors compose iterative adjustments to model predictions. After
initializing a tailor with this function, add adjustment specifications
with adjust_*() functions:
For probability distributions:
adjust_probability_calibration()For transformation of probabilities to hard class predictions:
adjust_probability_threshold(),adjust_equivocal_zone()For numeric distributions:
adjust_numeric_calibration(),adjust_numeric_range()
For ad-hoc adjustments, see adjust_predictions_custom().
Tailors must be trained with fit() before being applied to
new data with predict(). Tailors are tightly integrated
with the tidymodels framework; for greatest ease
of use, situate tailors in model workflows with ?workflows::add_tailor().
Ordering of adjustments
When composing multiple adjustments in a tailor object, the order matters and must follow specific rules depending on the type of predictions being adjusted (classification or regression).
For classification problems (binary and multiclass), adjustments that modify
probability estimates (e.g., adjust_probability_calibration()) must be
applied before adjustments that change hard class predictions (including
adjust_equivocal_zone()). This ensures that class predictions are based
on the final calibrated probabilities.
For regression problems, adjust_numeric_calibration() must be applied
before other numeric adjustments. This ensures that subsequent adjustments
work with calibrated predictions.
Generally, adjustments cannot be duplicated (i.e. the same adjustment type
cannot be used multiple times in a tailor object), though
adjust_predictions_custom() can be used multiple times. Adjustments for
different prediction types cannot be mixed—numeric adjustments (for
regression) and probability adjustments (for classification) cannot be
used in the same tailor object.
If these ordering rules are violated, tailor() will raise an
error describing the issue.
Examples
library(dplyr)
library(modeldata)
# `predicted` gives hard class predictions based on probabilities
two_class_example %>% count(predicted)
#> predicted n
#> 1 Class1 277
#> 2 Class2 223
# change the probability threshold to allot one class vs the other
tlr <-
tailor() %>%
adjust_probability_threshold(threshold = .1)
tlr
#>
#> ── tailor ──────────────────────────────────────────────────────────────────────
#> A binary postprocessor with 1 adjustment:
#>
#> • Adjust probability threshold to 0.1.
# fit by supplying column names. situate in a modeling workflow
# with `workflows::add_tailor()` to avoid having to do so manually
tlr_fit <- fit(
tlr,
two_class_example,
outcome = c(truth),
estimate = c(predicted),
probabilities = c(Class1, Class2)
)
tlr_fit
#>
#> ── tailor ──────────────────────────────────────────────────────────────────────
#> A binary postprocessor with 1 adjustment:
#>
#> • Adjust probability threshold to 0.1. [trained]
# adjust hard class predictions
predict(tlr_fit, two_class_example) %>% count(predicted)
#> # A tibble: 2 × 2
#> predicted n
#> <fct> <int>
#> 1 Class1 362
#> 2 Class2 138