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
A double vector.
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.
A new importance weights vector.
importance_weights(c(1.5, 2.3, 10))
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