Evaluation.hpp File Reference

Free-function API for evaluating trained models — variable importance, out-of-bag diagnostics, and prediction error. More...

#include "stats/Metrics.hpp"
#include "utils/Types.hpp"
#include <memory>
#include <optional>

Go to the source code of this file.

Classes
struct	ppforest2::VariableImportance
	Grouped result of the variable importance measures. More...

Namespaces
namespace	ppforest2
	Binarization strategies for multiclass-to-binary reduction.

Functions
double	ppforest2::error (Model const &model, types::FeatureMatrix const &x, types::OutcomeVector const &y)
	Prediction error of model on data (x, y).
template<typename T>
double	ppforest2::error (std::unique_ptr< T > const &m, types::FeatureMatrix const &x, types::OutcomeVector const &y)
std::optional< double >	ppforest2::oob_error (Forest const &forest, types::FeatureMatrix const &x, types::GroupIdVector const &y)
	Convenience overload — accepts integer class labels for classification.
std::optional< double >	ppforest2::oob_error (Forest const &forest, types::FeatureMatrix const &x, types::OutcomeVector const &y)
	Out-of-bag error.
template<typename T>
std::optional< double >	ppforest2::oob_error (std::unique_ptr< T > const &m, types::FeatureMatrix const &x, types::GroupIdVector const &y)
template<typename T>
std::optional< double >	ppforest2::oob_error (std::unique_ptr< T > const &m, types::FeatureMatrix const &x, types::OutcomeVector const &y)
stats::ClassificationMetrics::Maybe	ppforest2::oob_metrics (ClassificationForest const &forest, types::FeatureMatrix const &x, types::OutcomeVector const &y)
	Out-of-bag metrics — sentinel-free summary of OOB performance.
stats::RegressionMetrics::Maybe	ppforest2::oob_metrics (RegressionForest const &forest, types::FeatureMatrix const &x, types::OutcomeVector const &y)
types::OutcomeVector	ppforest2::oob_predict (Forest const &forest, types::FeatureMatrix const &x)
	Out-of-bag predictions.
template<typename T>
types::OutcomeVector	ppforest2::oob_predict (std::unique_ptr< T > const &m, types::FeatureMatrix const &x)
VariableImportance	ppforest2::variable_importance (Forest const &forest, types::FeatureMatrix const &x, types::OutcomeVector const &y, int seed)
	Bundle all three VI measures for a forest.
template<typename T>
VariableImportance	ppforest2::variable_importance (std::unique_ptr< T > const &m, types::FeatureMatrix const &x)
template<typename T>
VariableImportance	ppforest2::variable_importance (std::unique_ptr< T > const &m, types::FeatureMatrix const &x, types::OutcomeVector const &y, int seed)
VariableImportance	ppforest2::variable_importance (Tree const &tree, types::FeatureMatrix const &x)
	Bundle the available VI measures for a single tree (VI2 only).
types::FeatureVector	ppforest2::vi_permuted (Forest const &forest, types::FeatureMatrix const &x, types::OutcomeVector const &y, int seed)
	VI1 — per-variable permuted importance.
template<typename T>
types::FeatureVector	ppforest2::vi_permuted (std::unique_ptr< T > const &m, types::FeatureMatrix const &x, types::OutcomeVector const &y, int seed)
types::FeatureVector	ppforest2::vi_projections (Forest const &forest, int n_vars, types::FeatureVector const *scale=nullptr)
	VI2 for a forest — averaged over non-degenerate trees.
template<typename T>
types::FeatureVector	ppforest2::vi_projections (std::unique_ptr< T > const &m, int n_vars, types::FeatureVector const *scale=nullptr)
types::FeatureVector	ppforest2::vi_projections (Tree const &tree, int n_vars, types::FeatureVector const *scale=nullptr)
	VI2 for a single tree — projection-coefficient importance.
types::FeatureVector	ppforest2::vi_weighted_projections (Forest const &forest, types::FeatureMatrix const &x, types::OutcomeVector const &y, types::FeatureVector const *scale=nullptr)
	VI3 — weighted projection-coefficient importance.
template<typename T>
types::FeatureVector	ppforest2::vi_weighted_projections (std::unique_ptr< T > const &m, types::FeatureMatrix const &x, types::OutcomeVector const &y, types::FeatureVector const *scale=nullptr)

Detailed Description

Free-function API for evaluating trained models — variable importance, out-of-bag diagnostics, and prediction error.

Models (Tree, Forest and their concrete subclasses) own structure and prediction; everything else lives here as free functions that take a Model const&. Mode-specific dispatch goes through Model::Visitor, so callers don't need to know whether a forest is classification or regression — wrong-mode inputs raise UserError.

Mirrors the pattern already used by predict_proportions (in Model.hpp) and is_leaf (in TreeNode.hpp).