importance_weights: Importance weights

In hardhat: Construct Modeling Packages

Importance weights

Description

importance_weights() creates a vector of importance weights which allow you to apply a context dependent weight to your observations. Importance weights are supplied as a non-negative double vector, where fractional values are allowed.

Usage

importance_weights(x)

Arguments

x	A double vector.

Details

Importance weights focus on how much each row of the data set should influence model estimation. These can be based on data or arbitrarily set to achieve some goal.

In tidymodels, importance weights only affect the model estimation and supervised recipes steps. They are not used with yardstick functions for calculating measures of model performance.

Value

A new importance weights vector.

See Also

frequency_weights()

Examples

importance_weights(c(1.5, 2.3, 10))

hardhat documentation built on March 31, 2023, 10:21 p.m.