Struct FittedGradientBoostingClassifier 

pub struct FittedGradientBoostingClassifier<F> { /* private fields */ }

A fitted gradient boosting classifier.

For binary classification, stores a single sequence of trees predicting log-odds. For multiclass, stores K sequences of trees (one per class).

Implementations

impl<F: Float + Send + Sync + 'static> FittedGradientBoostingClassifier<F>

pub fn init(&self) -> &[F]

Returns the initial predictions per class (log-odds or log-prior).

pub fn learning_rate(&self) -> F

Returns the learning rate used during training.

pub fn trees(&self) -> &[Vec<Vec<Node<F>>>]

Returns a reference to the tree ensemble.

For binary classification, trees()[0] contains all trees. For multiclass, trees()[k] contains trees for class k.

pub fn n_features(&self) -> usize

Returns the number of features the model was trained on.

pub fn score(&self, x: &Array2<F>, y: &Array1<usize>) -> Result<F, FerroError>

Mean accuracy on the given test data and labels. Equivalent to sklearn’s ClassifierMixin.score.

Errors

Returns FerroError::ShapeMismatch if x.nrows() != y.len() or the feature count does not match the training data.

pub fn predict_proba(&self, x: &Array2<F>) -> Result<Array2<F>, FerroError>

Predict class probabilities. Mirrors sklearn’s GradientBoostingClassifier.predict_proba.

Binary: applies the logistic link to the cumulative log-odds. Multiclass: softmax over K cumulative scores.

Returns shape (n_samples, n_classes); rows sum to 1.

Errors

Returns FerroError::ShapeMismatch if the number of features does not match the fitted model.

pub fn predict_log_proba(&self, x: &Array2<F>) -> Result<Array2<F>, FerroError>

Element-wise log of predict_proba. Mirrors sklearn’s ClassifierMixin.predict_log_proba.

Errors

Forwards any error from predict_proba.

pub fn decision_function(&self, x: &Array2<F>) -> Result<Array2<F>, FerroError>

Cumulative raw scores per sample (pre-link). Mirrors sklearn’s GradientBoostingClassifier.decision_function.

Binary: shape (n_samples, 1) containing the cumulative log-odds. Multiclass: shape (n_samples, n_classes) containing per-class cumulative scores. (sklearn returns shape (n_samples,) for the binary case; ferrolearn keeps a 2-D shape for type-uniformity.)

Errors

Returns FerroError::ShapeMismatch if the number of features does not match the fitted model.

Trait Implementations

impl<F: Clone> Clone for FittedGradientBoostingClassifier<F>

fn clone(&self) -> FittedGradientBoostingClassifier<F>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<F: Debug> Debug for FittedGradientBoostingClassifier<F>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<F: Float + Send + Sync + 'static> HasClasses for FittedGradientBoostingClassifier<F>

fn classes(&self) -> &[usize]

Returns the sorted list of unique class labels.

fn n_classes(&self) -> usize

Returns the number of distinct classes.

impl<F: Float + Send + Sync + 'static> HasFeatureImportances<F> for FittedGradientBoostingClassifier<F>

fn feature_importances(&self) -> &Array1<F>

Returns a reference to the feature importance array.

impl<F: Float + Send + Sync + 'static> Predict<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>> for FittedGradientBoostingClassifier<F>

fn predict(&self, x: &Array2<F>) -> Result<Array1<usize>, FerroError>

Predict class labels.

Errors

Returns FerroError::ShapeMismatch if the number of features does not match the fitted model.

type Output = ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>>

The prediction output type (e.g., ndarray::Array1<F> or ndarray::Array1<usize>).

type Error = FerroError

The error type returned by predict.