tailor (version 0.1.0)

adjust_numeric_range: Truncate the range of numeric predictions

Description

Truncating ranges involves limiting the output of a model to a specific range of values, typically to avoid extreme or unrealistic predictions. This technique can help improve the practical applicability of a model's outputs by constraining them within reasonable bounds based on domain knowledge or physical limitations.

Usage

adjust_numeric_range(x, lower_limit = -Inf, upper_limit = Inf)

Value

An updated tailor() containing the new operation.

Arguments

x

A tailor().

upper_limit, lower_limit

A numeric value, NA (for no truncation) or hardhat::tune().

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

if (FALSE) { # rlang::is_installed(c("probably"))
library(tibble)

# create example data
set.seed(1)
d <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))
d

# specify calibration
tlr <-
  tailor() |>
  adjust_numeric_range(lower_limit = 1)

# train tailor by passing column names.
tlr_fit <- fit(tlr, d, outcome = y, estimate = y_pred)

predict(tlr_fit, d)
}