Struct GradientBoostingClassifier 

#[non_exhaustive]
pub struct GradientBoostingClassifier { /* private fields */ }

Gradient Boosting for classification (binary + multiclass).

• Binary: fits a single sequence of trees to log-loss pseudo-residuals.
• Multiclass (K > 2): fits K sequences of trees (one-vs-all softmax).

Uses Newton-Raphson leaf correction (second-order gradient step) for optimal leaf values, matching sklearn’s GradientBoostingClassifier.

Example

use scry_learn::dataset::Dataset;
use scry_learn::tree::GradientBoostingClassifier;

// Simple linearly separable data.
let features = vec![
    vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
    vec![0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
];
let target = vec![0.0, 0.0, 0.0, 1.0, 1.0, 1.0];
let data = Dataset::new(features, target, vec!["x1".into(), "x2".into()], "class");

let mut gbc = GradientBoostingClassifier::new()
    .n_estimators(50)
    .learning_rate(0.1)
    .max_depth(2);
gbc.fit(&data).unwrap();

let preds = gbc.predict(&[vec![1.5, 0.15], vec![5.5, 0.55]]).unwrap();
assert_eq!(preds[0], 0.0);
assert_eq!(preds[1], 1.0);

Implementations

impl GradientBoostingClassifier

pub fn new() -> Self

Create a new classifier with default parameters.

pub fn n_estimators(self, n: usize) -> Self

Set number of boosting rounds.

pub fn learning_rate(self, lr: f64) -> Self

Set learning rate (shrinkage).

pub fn max_depth(self, d: usize) -> Self

Set maximum depth per tree.

pub fn min_samples_split(self, n: usize) -> Self

Set minimum samples required to split.

pub fn min_samples_leaf(self, n: usize) -> Self

Set minimum samples required in a leaf.

pub fn subsample(self, s: f64) -> Self

Set subsample fraction for stochastic GBT.

pub fn seed(self, s: u64) -> Self

Set random seed.

pub fn class_weight(self, cw: ClassWeight) -> Self

Set class weighting strategy for imbalanced datasets.

pub fn callback(self, cb: Box<dyn TrainingCallback>) -> Self

Add a training callback (invoked after each boosting round).

pub fn fit(&mut self, data: &Dataset) -> Result<()>

Train the gradient boosting classifier.

pub fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict class labels for new samples.

pub fn predict_proba(&self, features: &[Vec<f64>]) -> Result<Vec<Vec<f64>>>

Predict class probabilities for new samples.

pub fn feature_importances(&self) -> Result<Vec<f64>>

Feature importances averaged across all trees.

pub fn n_classes(&self) -> usize

Number of classes.

pub fn n_trees(&self) -> usize

Total number of trees across all class sequences.

pub fn history(&self) -> Option<&TrainingHistory>

Return training history (populated after fit()).

pub fn class_trees(&self) -> &[Vec<DecisionTreeRegressor>]

Get tree sequences per class (for inspection or ONNX export). class_trees()[class_idx][estimator_idx] is the tree for that class/round.

pub fn n_features(&self) -> usize

Number of features the model was trained on.

pub fn learning_rate_val(&self) -> f64

Learning rate value.

pub fn init_predictions_val(&self) -> &[f64]

Initial predictions per class.

Trait Implementations

impl CalibrableClassifier for GradientBoostingClassifier

fn fit(&mut self, data: &Dataset) -> Result<()>

Train on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict class labels.

fn predict_proba(&self, features: &[Vec<f64>]) -> Result<Vec<Vec<f64>>>

Predict class probabilities. Returns [n_samples][n_classes].

fn clone_box(&self) -> Box<dyn CalibrableClassifier>

Clone into a boxed trait object.

impl Clone for GradientBoostingClassifier

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Default for GradientBoostingClassifier

fn default() -> Self

Returns the “default value” for a type.

impl PipelineModel for GradientBoostingClassifier

fn fit(&mut self, data: &Dataset) -> Result<()>

Train the model on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict on row-major feature matrix.

impl Tunable for GradientBoostingClassifier

fn set_param(&mut self, name: &str, _value: ParamValue) -> Result<()>

Apply a named hyperparameter.

fn clone_box(&self) -> Box<dyn Tunable>

Clone this model into a boxed trait object.

fn fit(&mut self, data: &Dataset) -> Result<()>

Train on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict on row-major features.