% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ppmodel.R
\name{permuted_importance}
\alias{permuted_importance}
\title{Permuted variable importance for a random forest.}
\usage{
permuted_importance(model)
}
\arguments{
\item{model}{A \code{pprf} forest model.}
}
\value{
A numeric vector, one entry per feature. Negative values are
  meaningful (see Sign semantics above).
}
\description{
For each feature, measures the drop in OOB accuracy (classification) or
the increase in normalised MSE (regression) after randomly permuting
that feature across the OOB observations. Computed lazily from the
training data stored on the model; the result is cached.
}
\details{
**Sign semantics.** Entries may be **negative**. That is not an error
and not a sentinel: it means permuting the feature did not degrade OOB
fit on average — the feature's signal sits at or below the noise floor
of the permutation procedure. Interpret negative or near-zero entries
as "no evidence of importance"; rely on the ranking rather than
clipping at zero or normalizing. The scale is already comparable
within a fitted model.
}
