Compute nonparametric estimates of the chosen measure of predictiveness.
cv_predictiveness_point_est(
fitted_values,
y,
weights = rep(1, length(y)),
folds,
type = "r_squared",
na.rm = FALSE
)fitted values from a regression function; a list of length V, where each object is a set of predictions on the validation data.
the outcome.
weights for the computed influence curve (e.g., inverse probability weights for coarsened-at-random settings)
the cross-validation folds
which parameter are you estimating (defaults to anova, for ANOVA-based variable importance)?
logical; should NA's be removed in computation? (defaults to FALSE)
The estimated measure of predictiveness.
See the paper by Williamson, Gilbert, Simon, and Carone for more details on the mathematics behind this function and the definition of the parameter of interest.