Function plot_feature_importance

crates/openquant/src/feature_importance.rs:191–205

(
    importance: &BTreeMap<String, ImportanceStats>,
    oob_score: f64,
    oos_score: f64,
    output_path: Option<&str>,
)

/// is fitted on the training rows (with their `sample_weight`), the test rows are scored, and
/// then each feature column is shuffled in turn within the test rows and scored again. The
/// per-fold importance is `(base - perm) / (0 - perm)` for [`Scoring::NegLogLoss`] and
/// `(base - perm) / (1 - perm)` for [`Scoring::Accuracy`] and [`Scoring::F1`]; it is 0 when
/// the denominator is 0 or the ratio is not finite. Test-fold scores **are** weighted by
/// `sample_weight`. Accuracy and F1 use [`SimpleClassifier::predict`] (so an override is
/// honoured), negative log loss uses [`SimpleClassifier::predict_proba`]. The result is the mean
/// over folds and its standard error (sample deviation, ddof 1, over `sqrt(n_folds)`).
///
/// 1 means damaging the feature destroyed everything the model had, 0 that the model did not
/// need it, negative that it did better without it. Because the shuffle stays within each test
/// fold, a very persistent feature is somewhat understated.
///
/// # Errors
///
/// - [`FeatureImportanceError::EmptyXy`] if `x` or `y` is empty.
/// - [`FeatureImportanceError::XyLengthMismatch`] if `x.len() != y.len()`.
/// - [`FeatureImportanceError::LengthMismatch`] (`"feature_names"`) if the first row of `x`
///   does not have one entry per feature name.
/// - [`FeatureImportanceError::RaggedX`] if the rows of `x` differ in length.