adjust_probability_threshold: Change the event threshold

In tailor: Iterative Steps for Postprocessing Model Predictions

Change the event threshold

Description

Many machine learning systems determine hard class predictions by first predicting the probability of an event and then predicting that an event will occur if its respective probability is above 0.5. This adjustment allows practitioners to determine hard class predictions using a threshold other than 0.5. By setting appropriate thresholds, one can balance the trade-off between different types of errors (such as false positives and false negatives) to optimize the model's performance for specific use cases.

Usage

adjust_probability_threshold(x, threshold = 0.5)

Arguments

x	A tailor().
threshold	A numeric value (between zero and one) or hardhat::tune().

Value

An updated tailor() containing the new operation.

Data Usage

This adjustment doesn't require estimation and, as such, the same data that's used to train it with fit() can be predicted on with predict(); fitting this adjustment just collects metadata on the supplied column names and does not risk data leakage.

Examples

library(modeldata)

# `predicted` gives hard class predictions based on probability threshold .5
head(two_class_example)

# use a threshold of .1 instead:
tlr <-
  tailor() |>
  adjust_probability_threshold(.1)

# fit by supplying column names.
tlr_fit <- fit(
  tlr,
  two_class_example,
  outcome = c(truth),
  estimate = c(predicted),
  probabilities = c(Class1, Class2)
)

# adjust hard class predictions
predict(tlr_fit, two_class_example) |> head()

tailor documentation built on Aug. 25, 2025, 9:50 a.m.