numeric-metrics: Numeric metrics

In yardstick: Tidy Characterizations of Model Performance

Numeric metrics

Description

Numeric metrics evaluate regression predictions where both truth and estimate are numeric. These metrics measure how close predicted values are to the true values.

Input requirements

• truth: numeric

• estimate: numeric

Available metrics

ccc()

Direction: maximize. Range: [-1, 1]

gini_coef()

Direction: maximize. Range: [0, 1]

huber_loss()

Direction: minimize. Range: [0, Inf]

huber_loss_pseudo()

Direction: minimize. Range: [0, Inf]

iic()

Direction: maximize. Range: [-1, 1]

mae()

Direction: minimize. Range: [0, Inf]

mape()

Direction: minimize. Range: [0, Inf]

mase()

Direction: minimize. Range: [0, Inf]

mpe()

Direction: zero. Range: [-Inf, Inf]

msd()

Direction: zero. Range: [-Inf, Inf]

mse()

Direction: minimize. Range: [0, Inf]

poisson_log_loss()

Direction: minimize. Range: [0, Inf]

rmse()

Direction: minimize. Range: [0, Inf]

rmse_relative()

Direction: minimize. Range: [0, Inf]

rpd()

Direction: maximize. Range: [0, Inf]

rpiq()

Direction: maximize. Range: [0, Inf]

rsq()

Direction: maximize. Range: [-Inf, 1]

rsq_trad()

Direction: maximize. Range: [0, 1]

smape()

Direction: minimize. Range: [0, 100]

See Also

quantile-metrics for quantile prediction metrics

vignette("metric-types") for an overview of all metric types

Examples

data("solubility_test")

head(solubility_test)

rmse(solubility_test, solubility, prediction)

yardstick documentation built on April 8, 2026, 1:06 a.m.